Project overview

Our project tested the influence of microbial communities on plant performance and how “home advantages” and environmental pressures alter plant-microbe interactions across a water resource gradient in Waimea Valley on the north shore of O’ahu, Hawai’i. We used the following inoculum sources:
1. low precipitation environment = dry inoculum source.
2. intermediate precipitation environment = moderate inoculum source.
3. high precipitation environment = wet inoculum source.

We used the inoculum sources from the natural elevation-precipitation gradient in a factorial design experiment where plants were exposed to collected soil inocula and subjected to varying watering regimes.

To accompish this, we used axenically cloned model plant system (strawberry, Fragaria virginiana) grown in sterile soil, inoculated with soil slurries from each of those sites, and subjected plants to reciprocal water treatments imposed at three levels of watering (dry, moderate, wet) based on the average precipitation of the inoculum source environment.

Markdown Figure 1. Inoculated strawberries in incubators.
Markdown Figure 1. Inoculated strawberries in incubators.

.

We hypothesized that plants inoculated with soil microbes would outperform non-inoculated plants across all treatments and microbes from the low elevation site would be the most beneficial for plant growth under all water conditions. In testing the “home advantage” hypothesis, we expected plants from each inoculum source would perform best in their familiar water environment (e.g., low-water site inocula source plants would grow best under dry-water conditions, high-water inocula source plants would grow best under wet-water conditions).

Plant Biomass

We assessed total biomass in plants, as well as the proportion of biomass in the roots and the shoots. Code here will:
- load root/shoot graph codes here. - make total biomass plots and root and shoot biomass proportion graphs

### Pair down raw data 
# raw data import
shootrootwithcontrol<-read.csv('data/strawberries_raw_full_data.csv',header=TRUE)
shootrootwithcontrol %>% drop_na()

#### # subset everything unnecessary

shootroot_step1removal<-subset(shootrootwithcontrol,TimePoint!="T0"&TimePoint!="Control"&Type!="Control")
shootroot_final<-shootroot_step1removal %>% drop_na()
shootroot_inoc23removed<-subset(shootroot_final,Inoculum!="2"&Inoculum!="3")
shootroot_inoc23removed$Inoculum<-as.factor(shootroot_inoc23removed$Inoculum)
shootroot_inoc23removed$Inoculum<-factor(shootroot_inoc23removed$Inoculum,levels=c("Dry Inoculation","Medium Inoculation","Wet Inoculation","No Inoculation"))

# rename levels
levels(shootroot_inoc23removed$Inoculum) <- list(
  Dry="Dry Inoculation", Moderate="Medium Inoculation", Wet="Wet Inoculation", Control="No Inoculation")

shootroot_inoc23removed$Water<-as.factor(shootroot_inoc23removed$Water)
shootroot_inoc23removed$Water<-factor(shootroot_inoc23removed$Water,levels=c("Low","Medium","High"))

# rename one level
levels(shootroot_inoc23removed$Water)[levels(shootroot_inoc23removed$Water)=="Medium"] <- "Moderate"

# final data for biomass 
write.csv(shootroot_inoc23removed, "data//final data_published/strawberries_final_data.csv")

shootsonlywithcontrol<-subset(shootroot_inoc23removed,Type!="Root")
rootsonlywithcontrol<-subset(shootroot_inoc23removed,Type!="Shoot")

# export the final subset data
write.csv(shootsonlywithcontrol, "output/shootsonlywithcontrol_final.csv")
write.csv(rootsonlywithcontrol, "output/rootsonlywithcontrol_final.csv")

We take the root biomass (g) and the shoot biomass (g) to = total biomass, then express the proportion of the biomass for the roots and the shoots relative to total biomass of the plant.

Stats for proportion above and belowground plant biomass Run models linear models (Type II SS) for proportion root or shoot biomass
- First we will run the linear model as lm(mass ~ Water*Inoculation)
- Then run post-hoc contrasts through emmeans (if significant effects observed)

Total Biomass

Make a graph of total biomass and proportion of root and shoot biomass
- Make a plot of total biomass This is FIGURE S2.
- For shoots : shoot biomass (g) / total biomass (g) This is FIGURE 1a.
- For roots : root biomass (g) / total biomass (g) This is FIGURE 1b.


shootsonlywithcontrol <- shootsonlywithcontrol %>% 
       dplyr::rename("shoot.mass..g" = "WeightGrams")
rootsonlywithcontrol <- rootsonlywithcontrol %>% 
       dplyr::rename("root.mass..g" = "WeightGrams")
shootsonly_premerge<-subset(shootsonlywithcontrol,select = -c(SampleID,TimePoint,Type,SampleNotes,Water,Inoculum,Replicate,PlantNotes,SampleType,Sample_or_Control))
rootshoot_merge <- merge(shootsonly_premerge,rootsonlywithcontrol,by="PlantID")

#View(rootshoot_merge)
rootshoot_merge <-subset(rootshoot_merge, select = -c(Type,SampleNotes,PlantNotes))
rootshoot_merge$total.biomass..g<-rootshoot_merge$shoot.mass..g+rootshoot_merge$root.mass..g
rootshoot_merge$shoot.prop<-rootshoot_merge$shoot.mass..g/rootshoot_merge$total.biomass..g
rootshoot_merge$root.prop<-rootshoot_merge$root.mass..g/rootshoot_merge$total.biomass..g

#########
### From here with rootshoot_merge, you can redo all the above to see what the data looks like

#hist(rootshoot_merge$root.prop)
#hist(rootshoot_merge$shoot.prop)

#View(rootshoot_merge)
longer_data <- rootshoot_merge %>%
  pivot_longer(shoot.prop:root.prop, names_to = "Type", values_to = "response")

longer_data$Type<-factor(longer_data$Type, levels=c("shoot.prop", "root.prop"))


# total biomass
## figure
total.biomass<-ggplot(longer_data, aes(Water, total.biomass..g, fill=Inoculum)) +
  geom_boxplot(outlier.size = 0.7, alpha=0.6)+
  ylim(0,1)+
  ylab("Total biomass (g)") +
  #scale_fill_manual(values=c("orangered2", "goldenrod", "darkolivegreen3", "mistyrose2"))+
  scale_fill_brewer(palette="Dark2")+
  theme_bw()+ theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=10),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=18),
    legend.key = element_rect(color=NA, fill=NA),
    panel.grid = element_blank())

total.biomass
**Figure S2.** Boxplots of plant total biomass after inoculation and water treatment.

Figure S2. Boxplots of plant total biomass after inoculation and water treatment. 

dev.copy(pdf, "figures/FigS2.total.biomass.pdf", height=6, width=6)
dev.off()

########## figure
prop.biomass<-ggplot(longer_data, aes(Water, response, fill=Inoculum)) +
  facet_wrap(.~Type)+
  geom_boxplot(outlier.size = 0.7, alpha=0.6)+
  ylab("Proportion of total biomass (g/g)") +
  ylim(0,1)+
  #scale_fill_manual(values=c("orangered2", "goldenrod", "darkolivegreen3", "mistyrose2"))+
  scale_fill_brewer(palette="Dark2")+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=18),
    legend.key = element_rect(color=NA, fill=NA),
    panel.grid = element_blank())


prop.biomass
**Figure 1.** Boxplots of the proportion of strawberry total biomass in shoots (a) and roots (b) after inoculation and water treatment in grams/grams (g/g).

Figure 1. Boxplots of the proportion of strawberry total biomass in shoots (a) and roots (b) after inoculation and water treatment in grams/grams (g/g). 

ggsave("figures/Fig1.prop.biomass.pdf", height=6, width=10)

Run models linear models (Type II SS) for total biomass, proportion of total biomass as shoots and roots - First we will run the linear model as lm(prop mass ~ Water*Inoculation) using 4 inoculations (3 sources, control) - Then run a model of inoculation presence absence (effect of inoculation relative to controls)

  • Overall…Total biomass only affected by water.
  • Prop. roots and shoots: Effects of water and inoculation (no interaction) for 4 inoculation tests.

This is Table S1a-part1 output. Run models for total biomass in all 4 inoculations (3 sources and control)

## This is table S1A
totalbiomass.mod<-lm(total.biomass..g~Water*Inoculum,data=rootshoot_merge)
Anova(totalbiomass.mod,type=2)
## Anova Table (Type II tests)
## 
## Response: total.biomass..g
##                Sum Sq  Df F value  Pr(>F)  
## Water          0.0712   2  1.4005 0.24985  
## Inoculum       0.2439   3  3.1976 0.02538 *
## Water:Inoculum 0.1295   6  0.8489 0.53435  
## Residuals      3.6101 142                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

total.bio<-emmeans(totalbiomass.mod,pairwise~Inoculum)
#multcomp::cld(total.bio, Letters=letters)

This is Table S1a-part2 output. Run models for total biomass in all presence or absence of inoculation

#### if we test for inoculated vs. non-inoculated comparison
rootshoot_merge$Inoc.pres.abs<-ifelse(rootshoot_merge$Inoculum=="Control", "Non.inoc", "Inoc")
totalbiomass.mod.PresAbs<-lm(total.biomass..g~Inoc.pres.abs,data=rootshoot_merge)
Anova(totalbiomass.mod.PresAbs) # no  effect
## Anova Table (Type II tests)
## 
## Response: total.biomass..g
##               Sum Sq  Df F value Pr(>F)
## Inoc.pres.abs 0.0143   1  0.5385 0.4642
## Residuals     4.0427 152

Shoot Biomass Proportion (g/g)

Model output for proportion biomass in shoots

This is Table S1b-part1 output Run models for shoot biomass in all 4 inoculations (3 sources and control) - Effect of water and inoculation

## This is table S1A
shoot.prop.mod<-lm(shoot.prop~Water*Inoculum,data=rootshoot_merge)
Anova(shoot.prop.mod,type=2)
## Anova Table (Type II tests)
## 
## Response: shoot.prop
##                 Sum Sq  Df F value    Pr(>F)    
## Water          0.07696   2  6.8470  0.001449 ** 
## Inoculum       0.27219   3 16.1448 4.387e-09 ***
## Water:Inoculum 0.03928   6  1.1649  0.328493    
## Residuals      0.79799 142                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

shoot.prop.wat<-emmeans(shoot.prop.mod,pairwise~Water)
#multcomp::cld(shoot.prop.wat, Letters=letters)

shoot.prop.inoc<-emmeans(shoot.prop.mod,pairwise~Inoculum)
#multcomp::cld(shoot.prop.inoc, Letters=letters)

Shoots: Run models for shoot biomass in all inoculation presence/absence. This is Table S1b-part2 output

#### if we test for inoculated vs. non-inoculated comparison
shoot.prop.mod.PresAbs<-lm(shoot.prop~Inoc.pres.abs,data=rootshoot_merge)
Anova(shoot.prop.mod.PresAbs) # different effect
## Anova Table (Type II tests)
## 
## Response: shoot.prop
##                Sum Sq  Df F value  Pr(>F)    
## Inoc.pres.abs 0.22589   1  35.004 2.1e-08 ***
## Residuals     0.98091 152                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(allEffects(shoot.prop.mod.PresAbs)) # higher in non-inoculated plants
**Figure.** Differences between inocualted and control plant shoot biomass proportion.

Figure. Differences between inocualted and control plant shoot biomass proportion.

Root Biomass Proportion (g/g)

Model output for proportion biomass in roots.

This is Table S1c-part1 output
Run models for root biomass in all 4 inoculations (3 sources and control)
- Effect of water and inoculation

## This is table S1B
root.prop.mod<-lm(root.prop~Water*Inoculum,data=rootshoot_merge)
Anova(root.prop.mod,type=2)
## Anova Table (Type II tests)
## 
## Response: root.prop
##                 Sum Sq  Df F value    Pr(>F)    
## Water          0.07696   2  6.8470  0.001449 ** 
## Inoculum       0.27219   3 16.1448 4.387e-09 ***
## Water:Inoculum 0.03928   6  1.1649  0.328493    
## Residuals      0.79799 142                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# roots: Water posthoc
root.posthoc.wat<-emmeans(root.prop.mod, ~Water)
#multcomp::cld(root.posthoc.wat, Letters=letters)

root.posthoc.inoc<-emmeans(root.prop.mod, ~Inoculum)
#multcomp::cld(root.posthoc.inoc, Letters=letters)

This is Table S1c-part2 output. Run models for root biomass in all inoculation presence/absence.

#### if we test for inoculated vs. non-inoculated comparison
root.prop.mod.PresAbs<-lm(root.prop~Inoc.pres.abs,data=rootshoot_merge)
Anova(root.prop.mod.PresAbs) # different effect
## Anova Table (Type II tests)
## 
## Response: root.prop
##                Sum Sq  Df F value  Pr(>F)    
## Inoc.pres.abs 0.22589   1  35.004 2.1e-08 ***
## Residuals     0.98091 152                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(allEffects(root.prop.mod.PresAbs)) # higher in inoculated plants
**Figure.** Differences between inocualted and control plant root biomass proportion.

Figure. Differences between inocualted and control plant root biomass proportion.

Plant Survival

At the end of the experiment (following inoculation and a period of water treatments) we assessed plant survival. Data was analyzed using a bias-reduced logistic regression.

This is Figure S2 and analysis output in Table S2
- First test treatment effects using all 4 inoculation (3 sources and control) treatments - Then test for effect of inoculation vs. uninoculated controls

Test differences in survival in inoculated vs. control plants (presence/absence). This pools all the inoculated plants and tests differences between presence-absence of source inoculation.

# as presence absence
glm.mort.pres.abs<-glm(mort.binary ~ Inoc.pres.abs, family=binomial, data=mortality.df, method=brglmFit)
#summary(glm.mort.pres.abs)
anova(glm.mort.pres.abs, test="Chisq") # p=0.009 for Inoculum
## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: mort.binary
## 
## Terms added sequentially (first to last)
## 
## 
##               Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                            167     86.475           
## Inoc.pres.abs  1   6.5017       166     79.973  0.01078 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This is Figure S3. Make a plot of survival data.

### final mortality data here


# can view aggregated data here
# aggregate(mort.binary ~ Inoculum, data=mortality.df, FUN=sum)

mortality_prop_data<-mortality.df %>%
  dplyr::group_by(Water, Inoculum) %>% 
  dplyr::summarize(prop = sum(mortality=="alive") / n())

mortality_prop_data$Water<-factor(mortality_prop_data$Water,
                                  levels=c("Low","Moderate","High"))

mortality_prop_data$Inoculum<-factor(mortality_prop_data$Inoculum,
                                        levels=c("Dry","Moderate","Wet","Control"))
### plot it
survival<-ggplot(mortality_prop_data,aes(Inoculum,prop,fill=Inoculum))+
  geom_bar(stat="identity", color="black", linewidth=0.3, alpha=0.7)+
  facet_wrap(.~Water)+
  ylab("Proportion Survival") +
  #scale_fill_manual(values=c("orangered2", "goldenrod", "darkolivegreen3", "mistyrose2"))+
  scale_fill_brewer(palette="Dark2")+
  xlab("Inoculum")+
  theme_bw() + theme(
    axis.text.x=element_text(color='black',size=8),
    axis.title = element_text(color='black',size=10),
    axis.ticks = element_line(color='black'),
    legend.position = "none",
    panel.grid = element_blank())

survival
**Figure S3.** The proportion of surviving strawberry plants inoculated with three different soil inoculum sources (dry, moderate, wet) and uninoculated controls after exposure to three water availability treatments (T1; low, moderate, high).

Figure S3. The proportion of surviving strawberry plants inoculated with three different soil inoculum sources (dry, moderate, wet) and uninoculated controls after exposure to three water availability treatments (T1; low, moderate, high). 

ggsave("figures/FigS3.Survivorship.pdf", height=6, width=6)

Fungi amplicon sequences

We will use the code chunks here to coherse and subset the data in and outsid of phyloseq to produce measures of alpha and beta diversity (total species richness and species turnover/community composition, respectively). - Import data and export a final csv of the OTU table

input <- load_taxa_table("data/ASV_table_postfiltering.txt", "data/map_loaded_strawberries.txt")
# 538 samples, 1894 ASVs

### all samples
otuTAB.all<-input$data_loaded
otuTAB.all <- as.data.frame(otuTAB.all)
otuTAB.transp.all<-t(otuTAB.all)
write.csv(otuTAB.all, "output/OTU.tab/otuTAB.all.csv")

#remove inoculum that we're not using, 350 samples remaining
input2 =filter_data(input, 'Inoculum', filter_vals = '2') #1619 ASVs
input3 =filter_data(input2, 'Inoculum', filter_vals = '3') #1379 ASVs, 350 samples

########### final data with treatments + controls to be used in analyses (= Inoculum 1, 4, 5, 6)
ASV.data.contr<-input3

### no inoc 2 and 3 but controls remain -- this is for SRA archive
otuTAB.winnowed<-ASV.data.contr$data_loaded
otuTAB.winnowed <- as.data.frame(otuTAB.winnowed)
otuTAB.transp.winn<-t(otuTAB.winnowed)
write.csv(otuTAB.transp.winn, "output/OTU.tab/otuTAB.winnow.csv")

Produce a rarefaction curve for fungi sequences, this is for T0 and T1, roots and shoots.
This is Figure S1 - this takes some time, so for ease it is not run here in the markdown knitr - refer to jpeg of output here.

# can be memory intensive, set to eval false for now
# make a rarefaction curve of winnowed data: excluding treatment 2 and 3 not used in the study, retaining the "controls"

# set to eval =FALSE since can take some time to run
rarecurve(otuTAB.transp.winn, xlab = "Sequencing Depth", col="mediumseagreen", ylab = "ASV count", label = FALSE, xlim=c(0,60000))

dev.copy(pdf, "figures/FigS1.T1T0.depth.pdf", height=5, width=8)
dev.off()
Figure S1. All sample rarefaction curve
Figure S1. All sample rarefaction curve
########## remove any controls (treatments only 1,4,5,6)
ASV.data = filter_data(ASV.data.contr, 'TimePoint', filter_vals = 'Control') 
#346 samples remaining

otuTAB.final<-ASV.data$data_loaded
otuTAB.final <- as.data.frame(otuTAB.final)
otuTAB.tranp.final<-t(otuTAB.final)
write.csv(otuTAB.tranp.final, "data/final data_published/otuTAB.final.csv")

# individual files
write.csv(ASV.data$data_loaded, "data/final data_published/ASV.data_loaded_final.csv")
write.csv(ASV.data$map_loaded, "data/final data_published/ASV.map_loaded_final.csv")
write.csv(ASV.data$taxonomy_loaded, "data/final data_published/ASV.tax_loaded_final.csv")

# final data with treatments to be used in analyses 'ASV.data'

#keep only time point1, 284 samples
ASV.T0.sh = filter_data(ASV.data, 'TimePoint', keep_vals = 'T0') # 62 samples
ASV.T1 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T1') # 284 samples
ASV.T1.rt = filter_data(ASV.T1, 'Type', keep_vals = 'Root') # 143 samples
ASV.T1.sh = filter_data(ASV.T1, 'Type', keep_vals = 'Shoot')# 141 samples

# all T1 data (post stress, roots and shoot)
T1.root.shoot<-ASV.T1

otuTAB.T1.root.shoot<-T1.root.shoot$data_loaded
otuTAB.T1.root.shoot <- as.data.frame (otuTAB.T1.root.shoot)
otuTAB.transp.t1.root.shoot<-t(otuTAB.T1.root.shoot)


#using pack: labdsv
# Hellinger Transformation. Be sure to filter first and then do the transformation

#Let's separate into roots and shoots for separate dataframes, valuable in analyses later on
roots_post_stress = filter_data(T1.root.shoot, 'Type', keep_vals = 'Root')
shoots_post_stress = filter_data(T1.root.shoot, 'Type', keep_vals = 'Shoot')

otuTAB.rootT1<-roots_post_stress$data_loaded
otuTAB.rootT1 <- as.data.frame (otuTAB.rootT1)
otuTABtranspose.root.T1<-t(otuTAB.rootT1)

hell.root.T1<-hellinger(otuTABtranspose.root.T1)
#View(hell)

roots_post_stress$data_loaded<-t(hell.root.T1)

### shoot T1 df
otuTAB.shootT1<-shoots_post_stress$data_loaded
otuTAB.shootT1 <- as.data.frame (otuTAB.shootT1)
otuTABtranspose.shoot.T1<-t(otuTAB.shootT1)
hell.shoot.T1<-hellinger(otuTABtranspose.shoot.T1)
#View(hell)

shoots_post_stress$data_loaded<-t(hell.shoot.T1)

T0 Shoot amplicon

Let’s look at T0 only. These are the Shoots, as roots were not sampled early on.

Figure S4: shoots T0 NMDS - Shoot fungal microbiomes prior to the start of water stress.

#Let's view the original input map first
#View(input$map_loaded)

# final data with treatments to be used in analyses 'ASV.data'

#keep only time point0, 62 samples
ASV.T0 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T0')

# all T0 data (pre stress shoots)
shoot.T0<-ASV.T0
otuTAB.T0.shoot <- as.data.frame (shoot.T0$data_loaded)
otuTAB.transp.T0.shoot<-t(otuTAB.T0.shoot)

### transform
hell.T0<-hellinger(otuTAB.transp.T0.shoot)

#View(hell)
shoot.T0$data_loaded<-t(hell.T0)

shoot.T0.mds<-metaMDS(t(shoot.T0$data_loaded))
shoot.T0.NMDS.df <- as.data.frame(scores(shoot.T0.mds)$sites) 
shoot.T0.NMDS.df$Water <- as.factor(shoot.T0$map_loaded$Water)
shoot.T0.NMDS.df$Inoculum <- as.factor(shoot.T0$map_loaded$Inoculum)


# change levels...
shoot.T0.NMDS.df$Inoculum <- as.factor(shoot.T0.NMDS.df$Inoculum)
levels(shoot.T0.NMDS.df$Inoculum)<-list(Dry="1", Moderate="4", Wet="5", Control="6")

# change levels...
shoot.T0.NMDS.df$Water <- as.factor(shoot.T0.NMDS.df$Water)
levels(shoot.T0.NMDS.df$Water) <- list(Low="1", Moderate="2", High="3")
# make the NMDS plot

Shoots.T0.NMDS<-ggplot(data=shoot.T0.NMDS.df,aes(x=NMDS1,y=NMDS2,color=Inoculum)) + 
  scale_color_brewer(palette="Dark2")+
  geom_point(data=shoot.T0.NMDS.df,
             aes(x=NMDS1,y=NMDS2,color=Inoculum), alpha=0.8) + 
  stat_ellipse(data=shoot.T0.NMDS.df,aes(x=NMDS1,y=NMDS2,color=Inoculum))+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    panel.grid = element_blank(),
    legend.key = element_rect(color=NA, fill=NA))

Shoots.T0.NMDS
**Figure S4.** NMDS representing fungal community composition in shoots of strawberry plants subject to three different inoculum sources (dry, moderate, wet) and uninoculated controls prior to exposure to three water availability treatments (Time 0, T0)

Figure S4. NMDS representing fungal community composition in shoots of strawberry plants subject to three different inoculum sources (dry, moderate, wet) and uninoculated controls prior to exposure to three water availability treatments (Time 0, T0) 

ggsave("figures/FigS4.Shoots.T0.NMDS.pdf", height=6, width=5.5)

Run PERMANOVA to test differeces among treatments in T0 Shoots (prior to start of water treatment).
Table S3. T0 shoot fungal microbiome

library(vegan)

#make the model
otuTAB <- as.data.frame (ASV.T0$data_loaded)
otuTABtranspose<-t(otuTAB)
mta.sh.T0<-ASV.T0$map_loaded

#do as factor, no water treatment at T0
mta.sh.T0$Inoculum <- as.factor(mta.sh.T0$Inoculum)

set.seed(138)
model.sh.T0<-adonis2(otuTABtranspose~Inoculum,data=mta.sh.T0,permutations=999,method="bray")
model.sh.T0 # no effects
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTABtranspose ~ Inoculum, data = mta.sh.T0, permutations = 999, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)
## Model     3   0.7726 0.06253 1.2895  0.159
## Residual 58  11.5835 0.93747              
## Total    61  12.3561 1.00000

T1 Shoot and Root amplicon

Analyze the shoots and roots for T1 (at experiment end), following the exposure of plants to water treatments.

Shoots amplicon at T1 This is Table 2 PERMANOVA testing water treatment and inoculum source effects on plant-associated fungal communities in shoot and root biomass.
- Shoot NMDS and Permanova first.

########### SHOOTS
#you need to first load the data and make it a data frame. Then transpose the data frame. Add the map loaded too
otuTAB.sh.T1 <- as.data.frame(shoots_post_stress$data_loaded)
otuTAB.transp.sh.T1<-t(otuTAB.sh.T1)
mta.sh.T1<-shoots_post_stress$map_loaded

#do as factor
mta.sh.T1$Water <- as.factor(mta.sh.T1$Water)
mta.sh.T1$Inoculum <- as.factor(mta.sh.T1$Inoculum)

## using vegan
#make the model
set.seed(567)
model1.sh.T1<-adonis2(otuTAB.transp.sh.T1~Water*Inoculum,data=mta.sh.T1,permutations=999,method="bray")
model1.sh.T1 # interaction signif
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTAB.transp.sh.T1 ~ Water * Inoculum, data = mta.sh.T1, permutations = 999, method = "bray")
##           Df SumOfSqs      R2      F Pr(>F)    
## Model     11   16.624 0.32623 5.6781  0.001 ***
## Residual 129   34.334 0.67377                  
## Total    140   50.958 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#posthocs in pairwise adonis
mtapairwise.sh.T1<-tidyr::unite(mta.sh.T1,c='water_inoculum',c('Water','Inoculum'),sep='-')

pair.mod.water.sh.T1<-pairwise.adonis(otuTAB.transp.sh.T1, factors=c(mtapairwise.sh.T1$water_inoculum))
pair.mod.water.sh.T1

# option if main effects were key
# pair.mod.sh.T1<-pairwise.adonis2(otuTAB.transp.sh.T1~Water/Inoculum, data=mta.sh.T1)

This is Figure S5. Shoot NMDS at T1. - run NMDS ordination for T1

# NMDS for shoots
shoot.mds.T1<-metaMDS(t(shoots_post_stress$data_loaded))
shoot.T1<-scores(shoot.mds.T1)
shoot.T1.NMDS.df <- as.data.frame(shoot.T1$sites) 
shoot.T1.NMDS.df$Water <- shoots_post_stress$map_loaded$Water
shoot.T1.NMDS.df$Inoculum <- shoots_post_stress$map_loaded$Inoculum


# Shoot T1 NMDS plot
# change levels...
shoot.T1.NMDS.df$Inoculum <- as.factor(shoot.T1.NMDS.df$Inoculum)
levels(shoot.T1.NMDS.df$Inoculum)<-list(Dry="1", Moderate="4", Wet="5", Control="6")

# change levels...
shoot.T1.NMDS.df$Water <- as.factor(shoot.T1.NMDS.df$Water)
levels(shoot.T1.NMDS.df$Water) <- list(Low="1", Moderate="2", High="3")
### plot
Shoots.T1.NMDS<-ggplot(data=shoot.T1.NMDS.df,
                  aes(x=NMDS1,y=NMDS2,shape=Water,color=Inoculum)) + 
  scale_color_brewer(palette="Dark2")+
  ggtitle("Shoots T1")+
  geom_point(data=shoot.T1.NMDS.df,
             aes(x=NMDS1,y=NMDS2,shape=Water,color=Inoculum), alpha=0.8) + 
  # add the point markers
  stat_ellipse(data=shoot.T1.NMDS.df,aes(x=NMDS1,y=NMDS2,lty=Water,color=Inoculum))+
  scale_linetype_manual(values=c(1,2,3)) +
   theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    legend.key = element_rect(fill=NA),
    panel.grid = element_blank())

Shoots.T1.NMDS
**Figure S5** NMDS representing fungal community composition in shoots of strawberry plants inoculated with three different soil inoculum sources (dry, moderate, wet) and an uninoculated control indicated by color after being exposed to three water availability treatments (low, moderate, high) indicated by shapes and line type.

Figure S5 NMDS representing fungal community composition in shoots of strawberry plants inoculated with three different soil inoculum sources (dry, moderate, wet) and an uninoculated control indicated by color after being exposed to three water availability treatments (low, moderate, high) indicated by shapes and line type. 

ggsave("figures/FigS5.Shoot.T1.NMDS.pdf", height=6, width=6)

Roots amplicon at T1. - Now run the same model testing for root NMDS and Permanova at T1.

########### ROOTS
#you need to first load the data and make it a data frame. Then transpose the data frame. Add the map loaded too
otuTAB.ro.T1 <- as.data.frame (roots_post_stress$data_loaded)
otuTAB.transp.ro.T1<-t(otuTAB.ro.T1)
mta.ro.T1<-roots_post_stress$map_loaded

#do as factor
mta.ro.T1$Water <- as.factor(mta.ro.T1$Water)
mta.ro.T1$Inoculum <- as.factor(mta.ro.T1$Inoculum)

#make the model
set.seed(214)
model1.ro.T1<-adonis2(otuTAB.transp.ro.T1~Water*Inoculum,data=mta.ro.T1,permutations=999,method="bray")
model1.ro.T1
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTAB.transp.ro.T1 ~ Water * Inoculum, data = mta.ro.T1, permutations = 999, method = "bray")
##           Df SumOfSqs     R2      F Pr(>F)    
## Model     11   25.811 0.4972 11.776  0.001 ***
## Residual 131   26.102 0.5028                  
## Total    142   51.913 1.0000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mtapairwise.ro.T1<-tidyr::unite(mta.ro.T1,c='water_inoculum',c('Water','Inoculum'),sep='-')

# water pairwise
pair.mod.water.ro.T1<-pairwise.adonis(otuTAB.transp.ro.T1, factors=c(mta.ro.T1$Water))
pair.mod.water.ro.T1

# inoc pairwise
pair.mod.inoc.ro.T1<-pairwise.adonis(otuTAB.transp.ro.T1, factors=c(mta.ro.T1$Inoculum))
pair.mod.inoc.ro.T1

Figure 2. Root NMDS at T1. Now run NMDS and plot ordination for fungal communities in ROOTS at T1, following the water treatment at experiment’s end.

# NMDS for roots
root.mds.T1<-metaMDS(t(roots_post_stress$data_loaded))
root.T1<-scores(root.mds.T1)
root.T1.NMDS.df <- as.data.frame(root.T1$sites) 
root.T1.NMDS.df$Water <- as.factor(roots_post_stress$map_loaded$Water)
root.T1.NMDS.df$Inoculum <- as.factor(roots_post_stress$map_loaded$Inoculum)

# change levels...
root.T1.NMDS.df$Inoculum <- as.factor(root.T1.NMDS.df$Inoculum)
levels(root.T1.NMDS.df$Inoculum)<-list(Dry="1", Moderate="4", Wet="5", Control="6")

# change levels...
root.T1.NMDS.df$Water <- as.factor(root.T1.NMDS.df$Water)
levels(root.T1.NMDS.df$Water) <- list(Low="1", Moderate="2", High="3")
### root T1 NMDS
Roots.T1.NMDS<-ggplot(data=root.T1.NMDS.df,
                      aes(x=NMDS1,y=NMDS2,shape=Water,color=Inoculum)) + 
  scale_color_brewer(palette="Dark2")+
  stat_ellipse(data=root.T1.NMDS.df,aes(x=NMDS1,y=NMDS2,lty=Water,color=Inoculum))+
  scale_linetype_manual(values=c(1,2,3)) +
  ggtitle("Roots T1")+
  geom_point(data=root.T1.NMDS.df,
             aes(x=NMDS1,y=NMDS2,shape=factor(Water),color=factor(Inoculum)), alpha=0.8) + 
  # add the point markers
 theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    legend.key = element_rect(fill=NA),
    panel.grid = element_blank())

Roots.T1.NMDS
**Figure 2.** NMDS representing changes in fungal community composition in shoots of strawberry plants inoculated with fungi originating from dry (a), moderate (b) and wet (c) environments within the Waimea watershed along with uninoculated controls (d) before (T0) and after (T1) being exposed to three water availability treatments (low, moderate and high)

Figure 2. NMDS representing changes in fungal community composition in shoots of strawberry plants inoculated with fungi originating from dry (a), moderate (b) and wet (c) environments within the Waimea watershed along with uninoculated controls (d) before (T0) and after (T1) being exposed to three water availability treatments (low, moderate and high) 

ggsave("figures/Fig2.Root.T1.NMDS.pdf", height=6, width=6)

T0-T1 Shoots vector NMDS

Refilter time. Select only shoots, compare how they change from T0 to T1. - reformat and subset the data. - check levels for each treatment and how they are called - once data has been subset we can run PERMANOVA

#Let's view the original input map first
#View(input$map_loaded)
#Start by removing all roots. We can only do a time one with shoots

# final data with treatments to be used in analyses 'ASV.data'

shoot.T0T1 = filter_data(ASV.data, 'Type', keep_vals = 'Shoot') 

### Then, let's set up the data for the NMDS plot:

#View(shoot.T0T1$map_loaded)
shoot.T0T1.mds<-metaMDS(t(shoot.T0T1$data_loaded))
shoot.T0T1.NMDS.df <- as.data.frame(scores(shoot.T0T1.mds)$sites) 

shoot.T0T1.NMDS.df$Water <- as.factor(shoot.T0T1$map_loaded$Water)
shoot.T0T1.NMDS.df$Inoculum <- as.factor(shoot.T0T1$map_loaded$Inoculum)
shoot.T0T1.NMDS.df$TimePoint <- as.factor(shoot.T0T1$map_loaded$TimePoint)
shoot.T0T1.NMDS.df$PlantID <- shoot.T0T1$map_loaded$PlantID

#envfit
shoot.T0T1$map_loaded<-subset(shoot.T0T1$map_loaded, select = -c(WeightGrams,SampleNotes, PlantNotes))
shoot.T0T1$map_loaded$Water<-as.factor(shoot.T0T1$map_loaded$Water)
shoot.T0T1$map_loaded$Inoculum<-as.factor(shoot.T0T1$map_loaded$Inoculum)
shoot.T0T1$map_loaded$TimeWater <- paste(shoot.T0T1$map_loaded$TimePoint,shoot.T0T1$map_loaded$Water)
shoot.T0T1$map_loaded$TimeInoculum <- paste(shoot.T0T1$map_loaded$TimePoint,shoot.T0T1$map_loaded$Inoculum)

waterfinalvf <- envfit(shoot.T0T1.mds~TimeWater, data=shoot.T0T1$map_loaded, perm = 999)
water.scrs.final <- as.data.frame(scores(waterfinalvf, display = "factors"))

inocfinalvf <- envfit(shoot.T0T1.mds~TimeInoculum, data=shoot.T0T1$map_loaded, perm = 999)
inoc.scrs.final <- as.data.frame(scores(inocfinalvf, display = "factors"))

############
# change levels...
shoot.T0T1.NMDS.df$Inoculum <- as.factor(shoot.T0T1.NMDS.df$Inoculum)
levels(shoot.T0T1.NMDS.df$Inoculum)<-list(Dry="1", Moderate="4", Wet="5", Control="6")

# change levels...
shoot.T0T1.NMDS.df$Water <- as.factor(shoot.T0T1.NMDS.df$Water)
levels(shoot.T0T1.NMDS.df$Water) <- list(Low="1", Moderate="2", High="3")

#########################
#shoots: T0 to T1
#you need to first load the data and make it a data frame. Then transpose the data frame. Add the map loaded too
otuTAB.sh.T0T1 <- as.data.frame(shoot.T0T1$data_loaded)
otuTAB.transp.sh.T0T1<-t(otuTAB.sh.T0T1)
mta.sh.T0T1<-shoot.T0T1$map_loaded

#do as factor
mta.sh.T0T1$Water <- as.factor(mta.sh.T0T1$Water)
mta.sh.T0T1$Inoculum <- as.factor(mta.sh.T0T1$Inoculum)
mta.sh.T0T1$TimePoint <- as.factor(mta.sh.T0T1$TimePoint)


####### separate by inoculum
inputshootsonly = filter_data(ASV.data, 'Type', keep_vals = 'Shoot') 

## #inoc 1
sh.T0T1.inoc1 = filter_data(inputshootsonly, 'Inoculum', keep_vals = '1') 
otuTAB.sh.T0T1.inoc1 <- as.data.frame (sh.T0T1.inoc1$data_loaded)
otuTAB.transp.sh.T0T1.inoc1<-t(otuTAB.sh.T0T1.inoc1)
mta.sh.T0T1.inoc1<-sh.T0T1.inoc1$map_loaded

mta.sh.T0T1.inoc1$Water.T0.T1<-as.factor(ifelse(
              mta.sh.T0T1.inoc1$TimePoint=="T1" & mta.sh.T0T1.inoc1$Water=="1", "Low",
                ifelse(mta.sh.T0T1.inoc1$TimePoint=="T1" & mta.sh.T0T1.inoc1$Water=="2", "Moderate",
                  ifelse(mta.sh.T0T1.inoc1$TimePoint=="T1" & mta.sh.T0T1.inoc1$Water=="3", "High",
                    ifelse(mta.sh.T0T1.inoc1$TimePoint=="T0" & mta.sh.T0T1.inoc1$Water=="1", "T0",
              ifelse(mta.sh.T0T1.inoc1$TimePoint=="T0" & mta.sh.T0T1.inoc1$Water=="2", "T0", "T0"))))))
#do as factor
make.fac<-c("Water", "Inoculum", "TimePoint", "Water.T0.T1")
mta.sh.T0T1.inoc1[make.fac]<-lapply(mta.sh.T0T1.inoc1[make.fac], factor)

mta.sh.T0T1.inoc1$Water.T0.T1 <- factor(mta.sh.T0T1.inoc1$Water.T0.T1, levels=c("T0", "Low", "Moderate", "High"))


### inoc 4
sh.T0T1.inoc4 = filter_data(inputshootsonly, 'Inoculum', keep_vals = '4')
otuTAB.sh.T0T1.inoc4 <- as.data.frame (sh.T0T1.inoc4$data_loaded)
otuTAB.transp.sh.T0T1.inoc4<-t(otuTAB.sh.T0T1.inoc4)
mta.sh.T0T1.inoc4<-sh.T0T1.inoc4$map_loaded

mta.sh.T0T1.inoc4$Water.T0.T1<-as.factor(ifelse(
              mta.sh.T0T1.inoc4$TimePoint=="T1" & mta.sh.T0T1.inoc4$Water=="1", "Low",
                ifelse(mta.sh.T0T1.inoc4$TimePoint=="T1" & mta.sh.T0T1.inoc4$Water=="2", "Moderate",
                  ifelse(mta.sh.T0T1.inoc4$TimePoint=="T1" & mta.sh.T0T1.inoc4$Water=="3", "High",
                    ifelse(mta.sh.T0T1.inoc4$TimePoint=="T0" & mta.sh.T0T1.inoc4$Water=="1", "T0",
              ifelse(mta.sh.T0T1.inoc4$TimePoint=="T0" & mta.sh.T0T1.inoc4$Water=="2", "T0", "T0"))))))

mta.sh.T0T1.inoc4[make.fac]<-lapply(mta.sh.T0T1.inoc4[make.fac], factor)

mta.sh.T0T1.inoc4$Water.T0.T1 <- factor(mta.sh.T0T1.inoc4$Water.T0.T1, levels=c("T0", "Low", "Moderate", "High"))


## #inoc 5
sh.T0T1.inoc5 = filter_data(inputshootsonly, 'Inoculum', keep_vals = '5')
otuTAB.sh.T0T1.inoc5 <- as.data.frame (sh.T0T1.inoc5$data_loaded)
otuTAB.transp.sh.T0T1.inoc5<-t(otuTAB.sh.T0T1.inoc5)
mta.sh.T0T1.inoc5<-sh.T0T1.inoc5$map_loaded

mta.sh.T0T1.inoc5$Water.T0.T1<-as.factor(ifelse(
              mta.sh.T0T1.inoc5$TimePoint=="T1" & mta.sh.T0T1.inoc5$Water=="1", "Low",
                ifelse(mta.sh.T0T1.inoc5$TimePoint=="T1" & mta.sh.T0T1.inoc5$Water=="2", "Moderate",
                  ifelse(mta.sh.T0T1.inoc5$TimePoint=="T1" & mta.sh.T0T1.inoc5$Water=="3", "High",
                    ifelse(mta.sh.T0T1.inoc5$TimePoint=="T0" & mta.sh.T0T1.inoc5$Water=="1", "T0",
              ifelse(mta.sh.T0T1.inoc5$TimePoint=="T0" & mta.sh.T0T1.inoc5$Water=="2", "T0", "T0"))))))

mta.sh.T0T1.inoc5[make.fac]<-lapply(mta.sh.T0T1.inoc5[make.fac], factor)

mta.sh.T0T1.inoc5$Water.T0.T1 <- factor(mta.sh.T0T1.inoc5$Water.T0.T1, levels=c("T0", "Low", "Moderate", "High"))

### inoc 6
sh.T0T1.inoc6 = filter_data(inputshootsonly, 'Inoculum', keep_vals = '6') 
otuTAB.sh.T0T1.inoc6 <- as.data.frame (sh.T0T1.inoc6$data_loaded)
otuTAB.transp.sh.T0T1.inoc6<-t(otuTAB.sh.T0T1.inoc6)
mta.sh.T0T1.inoc6<-sh.T0T1.inoc6$map_loaded

mta.sh.T0T1.inoc6$Water.T0.T1<-as.factor(ifelse(
              mta.sh.T0T1.inoc6$TimePoint=="T1" & mta.sh.T0T1.inoc6$Water=="1", "Low",
                ifelse(mta.sh.T0T1.inoc6$TimePoint=="T1" & mta.sh.T0T1.inoc6$Water=="2", "Moderate",
                  ifelse(mta.sh.T0T1.inoc6$TimePoint=="T1" & mta.sh.T0T1.inoc6$Water=="3", "High",
                    ifelse(mta.sh.T0T1.inoc6$TimePoint=="T0" & mta.sh.T0T1.inoc6$Water=="1", "T0",
              ifelse(mta.sh.T0T1.inoc6$TimePoint=="T0" & mta.sh.T0T1.inoc6$Water=="2", "T0", "T0"))))))

mta.sh.T0T1.inoc6[make.fac]<-lapply(mta.sh.T0T1.inoc6[make.fac], factor)

mta.sh.T0T1.inoc6$Water.T0.T1 <- factor(mta.sh.T0T1.inoc6$Water.T0.T1, levels=c("T0", "Low", "Moderate", "High"))

Data looks good! Now run a Permanova and posthoc tests for shoot fungal communities, for each of the inocula, and run pairwise tests (with hochberg corrections) to see which groups differ.

Dry inoculum source = inoc 1

############ inoc 1
mod1.sh.T0T1.inoc1<-adonis2(otuTAB.transp.sh.T0T1.inoc1~Water.T0.T1,data=mta.sh.T0T1.inoc1,permutations=999,method="bray")
mod1.sh.T0T1.inoc1
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTAB.transp.sh.T0T1.inoc1 ~ Water.T0.T1, data = mta.sh.T0T1.inoc1, permutations = 999, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)    
## Model     3   4.6043 0.22532 4.6537  0.001 ***
## Residual 48  15.8301 0.77468                  
## Total    51  20.4344 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#use pack pairwiseAdonis
set.seed(777)
pair.mod.sh.T1<-pairwise.adonis(otuTAB.transp.sh.T0T1.inoc1,factors=mta.sh.T0T1.inoc1$Water.T0.T1, 
                                p.adjust.m = "hochberg")
pair.mod.sh.T1
##              pairs Df SumsOfSqs  F.Model         R2 p.value p.adjusted sig
## 1  Low vs Moderate  1 0.5818870 1.567743 0.06131722   0.050      0.150    
## 2      Low vs High  1 0.4070313 1.172171 0.04849259   0.256      0.318    
## 3        Low vs T0  1 2.5641846 9.272375 0.27054954   0.001      0.004   *
## 4 Moderate vs High  1 0.4184080 1.079261 0.04482120   0.318      0.318    
## 5   Moderate vs T0  1 2.4008295 7.652296 0.23435706   0.001      0.004   *
## 6       High vs T0  1 2.6770422 9.281522 0.27887913   0.001      0.004   *

Moderate inoculum source = inoc 4

############ inoc 4
mod1.sh.T0T1.inoc4<-adonis2(otuTAB.transp.sh.T0T1.inoc4~Water.T0.T1,data=mta.sh.T0T1.inoc4,permutations=999,method="bray")
mod1.sh.T0T1.inoc4
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTAB.transp.sh.T0T1.inoc4 ~ Water.T0.T1, data = mta.sh.T0T1.inoc4, permutations = 999, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)    
## Model     3   6.6304 0.29916 7.1142  0.001 ***
## Residual 50  15.5332 0.70084                  
## Total    53  22.1636 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#use pack pairwiseAdonis
set.seed(808)
pair.mod.sh.T4<-pairwise.adonis(otuTAB.transp.sh.T0T1.inoc4,factors=mta.sh.T0T1.inoc4$Water.T0.T1, 
                                p.adjust.m = "hochberg")
pair.mod.sh.T4
##              pairs Df SumsOfSqs   F.Model         R2 p.value p.adjusted sig
## 1  Low vs Moderate  1 1.4851305  4.093043 0.16988484   0.001      0.001  **
## 2      Low vs High  1 0.9704179  2.343131 0.10487031   0.001      0.001  **
## 3        Low vs T0  1 2.1909691 10.110199 0.27998181   0.001      0.001  **
## 4 Moderate vs High  1 1.0611739  2.572853 0.09682262   0.001      0.001  **
## 5   Moderate vs T0  1 3.6812037 15.232259 0.33675654   0.001      0.001  **
## 6       High vs T0  1 3.0400922 11.019644 0.26864308   0.001      0.001  **

Wet inoculum source = inoc 5

########### inoc 5
mod1.sh.T0T1.inoc5<-adonis2(otuTAB.transp.sh.T0T1.inoc5~Water.T0.T1,data=mta.sh.T0T1.inoc5,permutations=999,method="bray")
mod1.sh.T0T1.inoc5
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = otuTAB.transp.sh.T0T1.inoc5 ~ Water.T0.T1, data = mta.sh.T0T1.inoc5, permutations = 999, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)    
## Model     3   4.4246 0.24944 4.6528  0.001 ***
## Residual 42  13.3132 0.75056                  
## Total    45  17.7378 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#use pack pairwiseAdonis
set.seed(805)
pair.mod.sh.T5<-pairwise.adonis(otuTAB.transp.sh.T0T1.inoc5,factors=mta.sh.T0T1.inoc5$Water.T0.T1,
                                p.adjust.m = "hochberg")
pair.mod.sh.T5
##              pairs Df SumsOfSqs   F.Model         R2 p.value p.adjusted sig
## 1  Low vs Moderate  1 1.1315972  3.241577 0.12352829   0.001      0.003   *
## 2      Low vs High  1 0.6345911  1.701137 0.08217603   0.029      0.029   .
## 3        Low vs T0  1 1.3791763  4.877850 0.19607202   0.001      0.003   *
## 4 Moderate vs High  1 0.8255046  2.371406 0.09730282   0.011      0.022   .
## 5   Moderate vs T0  1 2.7112908 10.016860 0.30338621   0.001      0.003   *
## 6       High vs T0  1 2.0941976  7.529970 0.28382882   0.001      0.003   *

Control inoculum source = inoc 6 (the uninoculated plants)

### inoc 6
mod1.sh.T0T1.inoc6<-adonis2(otuTAB.transp.sh.T0T1.inoc6~Water.T0.T1,data=mta.sh.T0T1.inoc6,permutations=999,method="bray")
mod1.sh.T0T1.inoc6

#use pack pairwiseAdonis
set.seed(972)
pair.mod.sh.T6<-pairwise.adonis(otuTAB.transp.sh.T0T1.inoc6,factors=mta.sh.T0T1.inoc6$Water.T0.T1,
                                p.adjust.m = "hochberg")

Now make NMDS for each inoculum and plot an NMDS with vectors connecting centroids

inputshootsonly = filter_data(ASV.data, 'Type', keep_vals = 'Shoot') 

inoc1NMDSmovement =filter_data(inputshootsonly, 'Inoculum', keep_vals = '1')
inoc4NMDSmovement =filter_data(inputshootsonly, 'Inoculum', keep_vals = '4')
inoc5NMDSmovement =filter_data(inputshootsonly, 'Inoculum', keep_vals = '5')
inoc6NMDSmovement =filter_data(inputshootsonly, 'Inoculum', keep_vals = '6')

###### make otu dataframes for each inoculum (shoots only)

otuTAB<-inoc1NMDSmovement$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
inoc1NMDSmovement$data_loaded<-t(hell)

otuTAB<-inoc4NMDSmovement$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
inoc4NMDSmovement$data_loaded<-t(hell)

otuTAB<-inoc5NMDSmovement$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
inoc5NMDSmovement$data_loaded<-t(hell)

otuTAB<-inoc6NMDSmovement$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
inoc6NMDSmovement$data_loaded<-t(hell)

######## #NMDS for each inoculum, shoots only
inoc1.mds<-metaMDS(t(inoc1NMDSmovement$data_loaded))
inoc1<-scores(inoc1.mds)
data.scoresinoc1 <- as.data.frame(inoc1$sites) 
data.scoresinoc1$Water <- inoc1NMDSmovement$map_loaded$Water
data.scoresinoc1$Inoculum <- inoc1NMDSmovement$map_loaded$Inoculum
data.scoresinoc1$TimePoint <- inoc1NMDSmovement$map_loaded$TimePoint
data.scoresinoc1$PlantID <- inoc1NMDSmovement$map_loaded$PlantID
inoc1NMDSmovement$map_loaded$TimeWater <- paste(inoc1NMDSmovement$map_loaded$TimePoint,inoc1NMDSmovement$map_loaded$Water)
inoc1vf <- envfit(inoc1.mds~TimeWater, data=inoc1NMDSmovement$map_loaded, perm = 999)
inoc1.scrs <- as.data.frame(scores(inoc1vf, display = "factors"))

inoc4.mds<-metaMDS(t(inoc4NMDSmovement$data_loaded))
inoc4<-scores(inoc4.mds)
data.scoresinoc4 <- as.data.frame(inoc4$sites) 
data.scoresinoc4$Water <- inoc4NMDSmovement$map_loaded$Water
data.scoresinoc4$Inoculum <- inoc4NMDSmovement$map_loaded$Inoculum
data.scoresinoc4$TimePoint <- inoc4NMDSmovement$map_loaded$TimePoint
data.scoresinoc4$PlantID <- inoc4NMDSmovement$map_loaded$PlantID
inoc4NMDSmovement$map_loaded$TimeWater <- paste(inoc4NMDSmovement$map_loaded$TimePoint,inoc4NMDSmovement$map_loaded$Water)
inoc4vf <- envfit(inoc4.mds~TimeWater, data=inoc4NMDSmovement$map_loaded, perm = 999)
inoc4.scrs <- as.data.frame(scores(inoc4vf, display = "factors"))

inoc5.mds<-metaMDS(t(inoc5NMDSmovement$data_loaded))
inoc5<-scores(inoc5.mds)
data.scoresinoc5 <- as.data.frame(inoc5$sites) 
data.scoresinoc5$Water <- inoc5NMDSmovement$map_loaded$Water
data.scoresinoc5$Inoculum <- inoc5NMDSmovement$map_loaded$Inoculum
data.scoresinoc5$TimePoint <- inoc5NMDSmovement$map_loaded$TimePoint
data.scoresinoc5$PlantID <- inoc5NMDSmovement$map_loaded$PlantID
inoc5NMDSmovement$map_loaded$TimeWater <- paste(inoc5NMDSmovement$map_loaded$TimePoint,inoc5NMDSmovement$map_loaded$Water)
inoc5vf <- envfit(inoc5.mds~TimeWater, data=inoc5NMDSmovement$map_loaded, perm = 999)
inoc5.scrs <- as.data.frame(scores(inoc5vf, display = "factors"))

inoc6.mds<-metaMDS(t(inoc6NMDSmovement$data_loaded))
inoc6<-scores(inoc6.mds)
data.scoresinoc6 <- as.data.frame(inoc6$sites) 
data.scoresinoc6$Water <- inoc6NMDSmovement$map_loaded$Water
data.scoresinoc6$Inoculum <- inoc6NMDSmovement$map_loaded$Inoculum
data.scoresinoc6$TimePoint <- inoc6NMDSmovement$map_loaded$TimePoint
data.scoresinoc6$PlantID <- inoc6NMDSmovement$map_loaded$PlantID
inoc6NMDSmovement$map_loaded$TimeWater <- paste(inoc6NMDSmovement$map_loaded$TimePoint,inoc6NMDSmovement$map_loaded$Water)
inoc6vf <- envfit(inoc6.mds~TimeWater, data=inoc6NMDSmovement$map_loaded, perm = 999)
inoc6.scrs <- as.data.frame(scores(inoc6vf, display = "factors"))

Figure 3. NMDS for each inoculum (separate) by water treatment and vectors from T0 to T1


# combine all NMDS dataframes
all.NMDS.inoc<-as.data.frame(rbind(data.scoresinoc1, data.scoresinoc4, data.scoresinoc5, data.scoresinoc6))

# set structure
make.fac<-c("Water", "Inoculum", "TimePoint")
all.NMDS.inoc[make.fac] <- lapply(all.NMDS.inoc[make.fac], factor) # make all these factors
all.NMDS.inoc$TimePoint<-factor(all.NMDS.inoc$TimePoint, levels=c("T0", "T1"))


# change levels...
all.NMDS.inoc$Inoculum <- as.factor(all.NMDS.inoc$Inoculum)
levels(all.NMDS.inoc$Inoculum)<-list(Dry="1", Moderate="4", Wet="5", Control="6")

# change levels...
all.NMDS.inoc$Water <- as.factor(all.NMDS.inoc$Water)
levels(all.NMDS.inoc$Water) <- list(Low="1", Moderate="2", High="3")


# make new level
all.NMDS.inoc$Water.T0.T1<-as.factor(ifelse(
              all.NMDS.inoc$TimePoint=="T1" & all.NMDS.inoc$Water=="Low", "Low",
                ifelse(all.NMDS.inoc$TimePoint=="T1" & all.NMDS.inoc$Water=="Moderate", "Moderate",
                  ifelse(all.NMDS.inoc$TimePoint=="T1" & all.NMDS.inoc$Water=="High", "High",
                    ifelse(all.NMDS.inoc$TimePoint=="T0" & all.NMDS.inoc$Water=="Low", "T0",
              ifelse(all.NMDS.inoc$TimePoint=="T0" & all.NMDS.inoc$Water=="Moderate", "T0", "T0"))))))


all.NMDS.inoc$Water.T0.T1 <- factor(all.NMDS.inoc$Water.T0.T1, levels=c("T0", "Low", "Moderate", "High"))
                                       
####### Inoculum 1 centroids
NMDS1.mean.inoc1<-aggregate(NMDS1~ Water*TimePoint, data=data.scoresinoc1, FUN=mean)
NMDS2.mean.inoc1<-aggregate(NMDS2~ Water*TimePoint, data=data.scoresinoc1, FUN=mean)
centroid.inoc1<-as.data.frame(cbind(NMDS1.mean.inoc1, NMDS2.mean.inoc1[3]))
centroid.inoc1$TimePoint<-factor(centroid.inoc1$TimePoint, levels=c("T0", "T1"))
centroid.inoc1$Water<-as.factor(centroid.inoc1$Water)

# centroid for Water 0
NMDS1.W0.inoc1<-aggregate(NMDS1~ TimePoint, data=data.scoresinoc1, FUN=mean)
NMDS2.W0.inoc1<-aggregate(NMDS2~ TimePoint, data=data.scoresinoc1, FUN=mean)
centroid.W0.inoc1<-as.data.frame(cbind(NMDS1.W0.inoc1, NMDS2.W0.inoc1[2]))

# plot
inoc1.trajectory<-ggplot(subset(all.NMDS.inoc, Inoculum %in% "Dry"),
                         aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  ggtitle("Dry Inoculum")+
  geom_point(aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) +
  stat_ellipse(aes(x=NMDS1,y=NMDS2,color=factor(Water.T0.T1),linetype=factor(TimePoint)))+
  ylim(-2.2,2)+
  xlim(-2,2)+
  scale_color_manual(values=c("black", "paleturquoise3", "steelblue2", "skyblue4"))+
  scale_linetype_manual(values=c(2,1))+
  geom_segment(aes(x = centroid.W0.inoc1[1,2], xend = centroid.inoc1[4,3], 
                   y = centroid.W0.inoc1[1,3], yend = centroid.inoc1[4,4]), arrow = arrow(), colour = "paleturquoise3") +
  geom_segment(aes(x = centroid.W0.inoc1[1,2], xend = centroid.inoc1[5,3], 
                   y = centroid.W0.inoc1[1,3], yend = centroid.inoc1[5,4]),arrow = arrow(), colour = "steelblue2") +
  geom_segment(aes(x = centroid.W0.inoc1[1,2], xend = centroid.inoc1[6,3], 
                   y = centroid.W0.inoc1[1,3], yend = centroid.inoc1[6,4]),arrow = arrow(), colour = "skyblue4") +
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    panel.grid = element_blank(),
    legend.key = element_rect(fill=NA),
    legend.position = "none")
###

####### Moderate 4
NMDS1.mean.inoc4<-aggregate(NMDS1~ Water*TimePoint, data=data.scoresinoc4, FUN=mean)
NMDS2.mean.inoc4<-aggregate(NMDS2~ Water*TimePoint, data=data.scoresinoc4, FUN=mean)
centroid.inoc4<-as.data.frame(cbind(NMDS1.mean.inoc4, NMDS2.mean.inoc4[3]))
centroid.inoc4$TimePoint<-factor(centroid.inoc4$TimePoint, levels=c("T0", "T1"))
centroid.inoc4$Water<-as.factor(centroid.inoc4$Water)

# centroid for Water 0
NMDS1.W0.inoc4<-aggregate(NMDS1~ TimePoint, data=data.scoresinoc4, FUN=mean)
NMDS2.W0.inoc4<-aggregate(NMDS2~ TimePoint, data=data.scoresinoc4, FUN=mean)
centroid.W0.inoc4<-as.data.frame(cbind(NMDS1.W0.inoc4, NMDS2.W0.inoc4[2]))

#plot
inoc4.trajectory<-ggplot(subset(all.NMDS.inoc, Inoculum %in% "Moderate"),
                         aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  ggtitle("Moderate Inoculum")+
  geom_point(aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  stat_ellipse(aes(x=NMDS1,y=NMDS2,color=factor(Water.T0.T1),linetype=factor(TimePoint)))+
  ylim(-2.2,2)+
  xlim(-2,2)+
  scale_color_manual(values=c("black", "paleturquoise3", "steelblue2", "skyblue4"))+ 
  scale_linetype_manual(values=c(2,1))+
  geom_segment(aes(x = centroid.W0.inoc4[1,2], xend = centroid.inoc4[4,3], 
                   y = centroid.W0.inoc4[1,3], yend = centroid.inoc4[4,4]),arrow = arrow(), colour = "paleturquoise3") +
  geom_segment(aes(x = centroid.W0.inoc4[1,2], xend = centroid.inoc4[5,3], 
                   y = centroid.W0.inoc4[1,3], yend = centroid.inoc4[5,4]),arrow = arrow(), colour = "steelblue2") +
  geom_segment(aes(x = centroid.W0.inoc4[1,2], xend = centroid.inoc4[6,3], 
                   y = centroid.W0.inoc4[1,3], yend = centroid.inoc4[6,4]),arrow = arrow(), colour = "skyblue4") +
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    legend.key = element_rect(fill=NA),
    panel.grid = element_blank())

###

######### Inoculum 5

NMDS1.mean.inoc5<-aggregate(NMDS1~ Water*TimePoint, data=data.scoresinoc5, FUN=mean)
NMDS2.mean.inoc5<-aggregate(NMDS2~ Water*TimePoint, data=data.scoresinoc5, FUN=mean)
centroid.inoc5<-as.data.frame(cbind(NMDS1.mean.inoc5, NMDS2.mean.inoc5[3]))
centroid.inoc5$TimePoint<-factor(centroid.inoc5$TimePoint, levels=c("T0", "T1"))
centroid.inoc5$Water<-as.factor(centroid.inoc5$Water)

# centroid for Water 0
NMDS1.W0.inoc5<-aggregate(NMDS1~ TimePoint, data=data.scoresinoc5, FUN=mean)
NMDS2.W0.inoc5<-aggregate(NMDS2~ TimePoint, data=data.scoresinoc5, FUN=mean)
centroid.W0.inoc5<-as.data.frame(cbind(NMDS1.W0.inoc5, NMDS2.W0.inoc5[2]))

# plot
inoc5.trajectory<-ggplot(subset(all.NMDS.inoc, Inoculum %in% "Wet"),
                         aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  ggtitle("Wet Inoculum")+
  geom_point(aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  stat_ellipse(aes(x=NMDS1,y=NMDS2,color=factor(Water.T0.T1),linetype=factor(TimePoint)))+
  ylim(-2.2,2)+
  xlim(-2,2)+
  scale_color_manual(values=c("black", "paleturquoise3", "steelblue2", "skyblue4"))+ 
  scale_linetype_manual(values=c(2,1))+
  geom_segment(aes(x = centroid.W0.inoc5[1,2], xend = centroid.inoc5[4,3], 
                   y = centroid.W0.inoc5[1,3], yend = centroid.inoc5[4,4]),arrow = arrow(), colour = "paleturquoise3") +
  geom_segment(aes(x = centroid.W0.inoc5[1,2], xend = centroid.inoc5[5,3], 
                   y = centroid.W0.inoc5[1,3], yend = centroid.inoc5[5,4]),arrow = arrow(), colour = "steelblue2") +
  geom_segment(aes(x = centroid.W0.inoc5[1,2], xend = centroid.inoc5[6,3], 
                   y = centroid.W0.inoc5[1,3], yend = centroid.inoc5[6,4]),arrow = arrow(), colour = "skyblue4") +
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    legend.key = element_rect(fill=NA),
    panel.grid = element_blank(),
    legend.position = "none")

##

######### Inoculum 6

NMDS1.mean.inoc6<-aggregate(NMDS1~ Water*TimePoint, data=data.scoresinoc6, FUN=mean)
NMDS2.mean.inoc6<-aggregate(NMDS2~ Water*TimePoint, data=data.scoresinoc6, FUN=mean)
centroid.inoc6<-as.data.frame(cbind(NMDS1.mean.inoc6, NMDS2.mean.inoc6[3]))
centroid.inoc6$TimePoint<-factor(centroid.inoc6$TimePoint, levels=c("T0", "T1"))
centroid.inoc6$Water<-as.factor(centroid.inoc6$Water)

# centroid for Water 0
NMDS1.W0.inoc6<-aggregate(NMDS1~ TimePoint, data=data.scoresinoc6, FUN=mean)
NMDS2.W0.inoc6<-aggregate(NMDS2~ TimePoint, data=data.scoresinoc6, FUN=mean)
centroid.W0.inoc6<-as.data.frame(cbind(NMDS1.W0.inoc6, NMDS2.W0.inoc6[2]))

# plot
inoc6.trajectory<-ggplot(subset(all.NMDS.inoc, Inoculum %in% "Control"),
                         aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  ggtitle("Control")+
  geom_point(aes(x=NMDS1,y=NMDS2,shape=factor(TimePoint),color=factor(Water.T0.T1))) + 
  stat_ellipse(aes(x=NMDS1,y=NMDS2,color=factor(Water.T0.T1),linetype=factor(TimePoint)))+
  ylim(-2.2,2)+
  xlim(-2,2)+
  scale_color_manual(values=c("black", "paleturquoise3", "steelblue2", "skyblue4"))+ 
  scale_linetype_manual(values=c(2,1))+
  geom_segment(aes(x = centroid.W0.inoc6[1,2], xend = centroid.inoc6[4,3], 
                   y = centroid.W0.inoc6[1,3], yend = centroid.inoc6[4,4]),arrow = arrow(), colour = "paleturquoise3") +
  geom_segment(aes(x = centroid.W0.inoc6[1,2], xend = centroid.inoc6[5,3], 
                   y = centroid.W0.inoc6[1,3], yend = centroid.inoc6[5,4]),arrow = arrow(), colour = "steelblue2") +
  geom_segment(aes(x = centroid.W0.inoc6[1,2], xend = centroid.inoc6[6,3], 
                   y = centroid.W0.inoc6[1,3], yend = centroid.inoc6[6,4]),arrow = arrow(), colour = "skyblue4") +
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    legend.key = element_rect(fill=NA),
    panel.grid = element_blank(),
    legend.position = "none")


library(patchwork)
inocalone<-(inoc1.trajectory+inoc4.trajectory)/(inoc5.trajectory+inoc6.trajectory)

inocalone
**Figure 3.** NMDS representing changes in fungal community composition in shoots of strawberry plants inoculated with fungi originating from dry (a), moderate (b) and wet (c) environments within the Waimea watershed along with uninoculated controls (d) before (T0) and after (T1) being exposed to three water availability treatments (low, moderate and high). Black circles represent communities for each inoculum at T0, colored triangles at T1

Figure 3. NMDS representing changes in fungal community composition in shoots of strawberry plants inoculated with fungi originating from dry (a), moderate (b) and wet (c) environments within the Waimea watershed along with uninoculated controls (d) before (T0) and after (T1) being exposed to three water availability treatments (low, moderate and high). Black circles represent communities for each inoculum at T0, colored triangles at T1 

ggsave("figures/Fig3.T0T1.vector.pdf", height=8, width=8)

Now run a test of beta dispersion for each of the NMDS ordinations to see how dispersions changes from T0 to T1. Since we don’t have water treatments at T0 we are only comparing a change in beta dispersion over time in the shoots, pooled by each water treatment. Run betadisp, then pairwise comparisons.

Beta dispersion: Shoots T0 and T1 for 4 inoculums, This is Table 3.

Beta dispersion for inoculation 1 = dry source inocula

#### T0 and T1 shoots

# dry inoc
inoc1.dataframe <- as.data.frame(t(inoc1NMDSmovement$data_loaded))
groups <- inoc1NMDSmovement$map_loaded$TimePoint 
disinoc1 <- vegdist(inoc1.dataframe,method="bray")
bdinoc1<-betadisper(disinoc1,groups)
anova(bdinoc1)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## Groups     1 0.32760 0.32760  32.335 6.695e-07 ***
## Residuals 50 0.50657 0.01013                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# pairwise comparison
set.seed(123)
pairwise.beta.disper.results.inoc1 <- permutest(bdinoc1, pairwise = TRUE, iter=999)
pairwise.beta.disper.results.inoc1
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df  Sum Sq Mean Sq      F N.Perm Pr(>F)    
## Groups     1 0.32760 0.32760 32.335    999  0.001 ***
## Residuals 50 0.50657 0.01013                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##            T0    T1
## T0            0.001
## T1 6.6955e-07

Beta dispersion for inoculation 4 = moderate source inocula

# moderate inoc
inoc4.dataframe <- as.data.frame(t(inoc4NMDSmovement$data_loaded))
groups <- inoc4NMDSmovement$map_loaded$TimePoint
disinoc4 <- vegdist(inoc4.dataframe,method="bray")
bdinoc4<-betadisper(disinoc4,groups)
anova(bdinoc4)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## Groups     1 0.97751 0.97751  233.49 < 2.2e-16 ***
## Residuals 52 0.21770 0.00419                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

set.seed(213)
pairwise.beta.disper.results.inoc4 <- permutest(bdinoc4, pairwise = TRUE, iter=999)
pairwise.beta.disper.results.inoc4
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df  Sum Sq Mean Sq      F N.Perm Pr(>F)    
## Groups     1 0.97751 0.97751 233.49    999  0.001 ***
## Residuals 52 0.21770 0.00419                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##            T0    T1
## T0            0.001
## T1 7.1546e-21

Beta dispersion for inoculation 5 = wet-source inocula

# wet inoc
inoc5.dataframe <- as.data.frame(t(inoc5NMDSmovement$data_loaded))
groups <- inoc5NMDSmovement$map_loaded$TimePoint
disinoc5 <- vegdist(inoc5.dataframe,method="bray")
bdinoc5<-betadisper(disinoc5,groups)
anova(bdinoc5)
## Analysis of Variance Table
## 
## Response: Distances
##           Df Sum Sq  Mean Sq F value    Pr(>F)    
## Groups     1 0.2356 0.235604  45.608 2.631e-08 ***
## Residuals 44 0.2273 0.005166                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

set.seed(27)
pairwise.beta.disper.results.inoc5 <- permutest(bdinoc5, pairwise = TRUE, iter=999)
pairwise.beta.disper.results.inoc5
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df Sum Sq  Mean Sq      F N.Perm Pr(>F)    
## Groups     1 0.2356 0.235604 45.608    999  0.001 ***
## Residuals 44 0.2273 0.005166                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##            T0    T1
## T0            0.001
## T1 2.6308e-08

Beta dispersion for inoculation 6 = control

# control, uninoc
inoc6.dataframe <- as.data.frame(t(inoc6NMDSmovement$data_loaded))
groups <- inoc6NMDSmovement$map_loaded$TimePoint
disinoc6 <- vegdist(inoc6.dataframe,method="bray")
bdinoc6<-betadisper(disinoc6,groups)
anova(bdinoc6)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value    Pr(>F)    
## Groups     1 0.19108 0.191084  13.414 0.0006123 ***
## Residuals 49 0.69804 0.014246                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

set.seed(720)
pairwise.beta.disper.results.inoc6 <- permutest(bdinoc6, pairwise = TRUE, iter=9999)
pairwise.beta.disper.results.inoc6
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)    
## Groups     1 0.19108 0.191084 13.414    999  0.001 ***
## Residuals 49 0.69804 0.014246                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##            T0    T1
## T0            0.001
## T1 0.00061232

Shannon’s diversity

Examine Shannon’s diversity (alpha diversity, species richness) for the roots at T1 and the shoots at T0 and T1 - for each treatment roots at T1 - for each treatment shoots and T0 and T1

# inputraw has the slurry data so let's take that first. Then we will need to transform it too. 
# filtered data is 'ASV.data'

inputstep5 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T1')
roott1 = filter_data(inputstep5, 'Type', keep_vals = 'Root')

# T1: Roots, water 1
roott1water1 = filter_data(roott1, 'Water', keep_vals = '1')
roott1inoc1water1 = filter_data(roott1water1, 'Inoculum', keep_vals = '1')
roott1inoc4water1 = filter_data(roott1water1, 'Inoculum', keep_vals = '4')
roott1inoc5water1 = filter_data(roott1water1, 'Inoculum', keep_vals = '5')
roott1inoc6water1 = filter_data(roott1water1, 'Inoculum', keep_vals = '6')

# T1: Roots, water 2
roott1water2 = filter_data(roott1, 'Water', keep_vals = '2')
roott1inoc1water2 = filter_data(roott1water2, 'Inoculum', keep_vals = '1')
roott1inoc4water2 = filter_data(roott1water2, 'Inoculum', keep_vals = '4')
roott1inoc5water2 = filter_data(roott1water2, 'Inoculum', keep_vals = '5')
roott1inoc6water2 = filter_data(roott1water2, 'Inoculum', keep_vals = '6')

# T1: Roots, water 3
roott1water3 = filter_data(roott1, 'Water', keep_vals = '3')
roott1inoc1water3 = filter_data(roott1water3, 'Inoculum', keep_vals = '1')
roott1inoc4water3 = filter_data(roott1water3, 'Inoculum', keep_vals = '4')
roott1inoc5water3 = filter_data(roott1water3, 'Inoculum', keep_vals = '5')
roott1inoc6water3 = filter_data(roott1water3, 'Inoculum', keep_vals = '6')

# T1: Shoots, water 1
shoott1 = filter_data(inputstep5, 'Type', keep_vals = 'Shoot')
shoott1water1 = filter_data(shoott1, 'Water', keep_vals = '1')
shoott1inoc1water1 = filter_data(shoott1water1, 'Inoculum', keep_vals = '1')
shoott1inoc4water1 = filter_data(shoott1water1, 'Inoculum', keep_vals = '4')
shoott1inoc5water1 = filter_data(shoott1water1, 'Inoculum', keep_vals = '5')
shoott1inoc6water1 = filter_data(shoott1water1, 'Inoculum', keep_vals = '6')

# T1: Shoots, water 2
shoott1water2 = filter_data(shoott1, 'Water', keep_vals = '2')
shoott1inoc1water2 = filter_data(shoott1water2, 'Inoculum', keep_vals = '1')
shoott1inoc4water2 = filter_data(shoott1water2, 'Inoculum', keep_vals = '4')
shoott1inoc5water2 = filter_data(shoott1water2, 'Inoculum', keep_vals = '5')
shoott1inoc6water2 = filter_data(shoott1water2, 'Inoculum', keep_vals = '6')

# T1: Shoots, water 3
shoott1water3 = filter_data(shoott1, 'Water', keep_vals = '3')
shoott1inoc1water3 = filter_data(shoott1water3, 'Inoculum', keep_vals = '1')
shoott1inoc4water3 = filter_data(shoott1water3, 'Inoculum', keep_vals = '4')
shoott1inoc5water3 = filter_data(shoott1water3, 'Inoculum', keep_vals = '5')
shoott1inoc6water3 = filter_data(shoott1water3, 'Inoculum', keep_vals = '6')

# T0
t0 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T0')
shoott0 = filter_data(t0, 'Type', keep_vals = 'Shoot')

# T0: Shoots, water T0
shoott0inoc1 = filter_data(shoott0, 'Inoculum', keep_vals = '1')
shoott0inoc4 = filter_data(shoott0, 'Inoculum', keep_vals = '4')
shoott0inoc5 = filter_data(shoott0, 'Inoculum', keep_vals = '5')
shoott0inoc6 = filter_data(shoott0, 'Inoculum', keep_vals = '6')

#Now that we have all three ready, let's transform them to match our other data:

otuTAB<-roott1inoc1water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc1water1$data_loaded<-t(hell)

otuTAB<-roott1inoc4water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc4water1$data_loaded<-t(hell)

otuTAB<-roott1inoc5water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc5water1$data_loaded<-t(hell)

otuTAB<-roott1inoc6water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc6water1$data_loaded<-t(hell)

otuTAB<-roott1inoc1water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc1water2$data_loaded<-t(hell)

otuTAB<-roott1inoc4water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc4water2$data_loaded<-t(hell)

otuTAB<-roott1inoc5water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc5water2$data_loaded<-t(hell)

otuTAB<-roott1inoc6water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc6water2$data_loaded<-t(hell)

otuTAB<-roott1inoc1water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc1water3$data_loaded<-t(hell)

otuTAB<-roott1inoc4water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc4water3$data_loaded<-t(hell)

otuTAB<-roott1inoc5water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc5water3$data_loaded<-t(hell)

otuTAB<-roott1inoc6water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
roott1inoc6water3$data_loaded<-t(hell)

otuTAB<-shoott1inoc1water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc1water1$data_loaded<-t(hell)

otuTAB<-shoott1inoc4water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc4water1$data_loaded<-t(hell)

otuTAB<-shoott1inoc5water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc5water1$data_loaded<-t(hell)

otuTAB<-shoott1inoc6water1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc6water1$data_loaded<-t(hell)

otuTAB<-shoott1inoc1water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc1water2$data_loaded<-t(hell)

otuTAB<-shoott1inoc4water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc4water2$data_loaded<-t(hell)

otuTAB<-shoott1inoc5water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc5water2$data_loaded<-t(hell)

otuTAB<-shoott1inoc6water2$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc6water2$data_loaded<-t(hell)

otuTAB<-shoott1inoc1water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc1water3$data_loaded<-t(hell)

otuTAB<-shoott1inoc4water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc4water3$data_loaded<-t(hell)

otuTAB<-shoott1inoc5water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc5water3$data_loaded<-t(hell)

otuTAB<-shoott1inoc6water3$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott1inoc6water3$data_loaded<-t(hell)

otuTAB<-shoott0inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott0inoc1$data_loaded<-t(hell)

otuTAB<-shoott0inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott0inoc4$data_loaded<-t(hell)

otuTAB<-shoott0inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott0inoc5$data_loaded<-t(hell)

otuTAB<-shoott0inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
#View(hell)
shoott0inoc6$data_loaded<-t(hell)
#all shannon's diversity
library(vegan)
roott1inoc1water1.shan<-diversity(roott1inoc1water1$data_loaded, index = "shannon")
roott1inoc4water1.shan<-diversity(roott1inoc4water1$data_loaded, index = "shannon")
roott1inoc5water1.shan<-diversity(roott1inoc5water1$data_loaded, index = "shannon")
roott1inoc6water1.shan<-diversity(roott1inoc6water1$data_loaded, index = "shannon")

roott1inoc1water2.shan<-diversity(roott1inoc1water2$data_loaded, index = "shannon")
roott1inoc4water2.shan<-diversity(roott1inoc4water2$data_loaded, index = "shannon")
roott1inoc5water2.shan<-diversity(roott1inoc5water2$data_loaded, index = "shannon")
roott1inoc6water2.shan<-diversity(roott1inoc6water2$data_loaded, index = "shannon")

roott1inoc1water3.shan<-diversity(roott1inoc1water3$data_loaded, index = "shannon")
roott1inoc4water3.shan<-diversity(roott1inoc4water3$data_loaded, index = "shannon")
roott1inoc5water3.shan<-diversity(roott1inoc5water3$data_loaded, index = "shannon")
roott1inoc6water3.shan<-diversity(roott1inoc6water3$data_loaded, index = "shannon")

shoott1inoc1water1.shan<-diversity(shoott1inoc1water1$data_loaded, index = "shannon")
shoott1inoc4water1.shan<-diversity(shoott1inoc4water1$data_loaded, index = "shannon")
shoott1inoc5water1.shan<-diversity(shoott1inoc5water1$data_loaded, index = "shannon")
shoott1inoc6water1.shan<-diversity(shoott1inoc6water1$data_loaded, index = "shannon")

shoott1inoc1water2.shan<-diversity(shoott1inoc1water2$data_loaded, index = "shannon")
shoott1inoc4water2.shan<-diversity(shoott1inoc4water2$data_loaded, index = "shannon")
shoott1inoc5water2.shan<-diversity(shoott1inoc5water2$data_loaded, index = "shannon")
shoott1inoc6water2.shan<-diversity(shoott1inoc6water2$data_loaded, index = "shannon")

shoott1inoc1water3.shan<-diversity(shoott1inoc1water3$data_loaded, index = "shannon")
shoott1inoc4water3.shan<-diversity(shoott1inoc4water3$data_loaded, index = "shannon")
shoott1inoc5water3.shan<-diversity(shoott1inoc5water3$data_loaded, index = "shannon")
shoott1inoc6water3.shan<-diversity(shoott1inoc6water3$data_loaded, index = "shannon")

shoott0inoc1.shan<-diversity(shoott0inoc1$data_loaded, index = "shannon")
shoott0inoc4.shan<-diversity(shoott0inoc4$data_loaded, index = "shannon")
shoott0inoc5.shan<-diversity(shoott0inoc5$data_loaded, index = "shannon")
shoott0inoc6.shan<-diversity(shoott0inoc6$data_loaded, index = "shannon")
#roott1inoc1water1.shan
roott1inoc1water1df<-as.data.frame(roott1inoc1water1.shan)
colnames(roott1inoc1water1df)[1] ="shannon"
water<-rep("water1",times=nrow(roott1inoc1water1df))
inoculum<-rep("inoc1",times=nrow(roott1inoc1water1df))
type<-rep("root",times=nrow(roott1inoc1water1df))
roott1inoc1water1dffinal<-cbind(roott1inoc1water1df,water,inoculum,type)

#roott1inoc4water1
roott1inoc4water1df<-as.data.frame(roott1inoc4water1.shan)
colnames(roott1inoc4water1df)[1] ="shannon"
water<-rep("water1",times=nrow(roott1inoc4water1df))
inoculum<-rep("inoc4",times=nrow(roott1inoc4water1df))
type<-rep("root",times=nrow(roott1inoc4water1df))
roott1inoc4water1dffinal<-cbind(roott1inoc4water1df,water,inoculum,type)

#roott1inoc5water1
roott1inoc5water1df<-as.data.frame(roott1inoc5water1.shan)
colnames(roott1inoc5water1df)[1] ="shannon"
water<-rep("water1",times=nrow(roott1inoc5water1df))
inoculum<-rep("inoc5",times=nrow(roott1inoc5water1df))
type<-rep("root",times=nrow(roott1inoc5water1df))
roott1inoc5water1dffinal<-cbind(roott1inoc5water1df,water,inoculum,type)

#roott1inoc6water1
roott1inoc6water1df<-as.data.frame(roott1inoc6water1.shan)
colnames(roott1inoc6water1df)[1] ="shannon"
water<-rep("water1",times=nrow(roott1inoc6water1df))
inoculum<-rep("inoc6",times=nrow(roott1inoc6water1df))
type<-rep("root",times=nrow(roott1inoc6water1df))
roott1inoc6water1dffinal<-cbind(roott1inoc6water1df,water,inoculum,type)

#roott1inoc1water2
roott1inoc1water2df<-as.data.frame(roott1inoc1water2.shan)
colnames(roott1inoc1water2df)[1] ="shannon"
water<-rep("water2",times=nrow(roott1inoc1water2df))
inoculum<-rep("inoc1",times=nrow(roott1inoc1water2df))
type<-rep("root",times=nrow(roott1inoc1water2df))
roott1inoc1water2dffinal<-cbind(roott1inoc1water2df,water,inoculum,type)

#roott1inoc4water2
roott1inoc4water2df<-as.data.frame(roott1inoc4water2.shan)
colnames(roott1inoc4water2df)[1] ="shannon"
water<-rep("water2",times=nrow(roott1inoc4water2df))
inoculum<-rep("inoc4",times=nrow(roott1inoc4water2df))
type<-rep("root",times=nrow(roott1inoc4water2df))
roott1inoc4water2dffinal<-cbind(roott1inoc4water2df,water,inoculum,type)

#roott1inoc5water2
roott1inoc5water2df<-as.data.frame(roott1inoc5water2.shan)
colnames(roott1inoc5water2df)[1] ="shannon"
water<-rep("water2",times=nrow(roott1inoc5water2df))
inoculum<-rep("inoc5",times=nrow(roott1inoc5water2df))
type<-rep("root",times=nrow(roott1inoc5water2df))
roott1inoc5water2dffinal<-cbind(roott1inoc5water2df,water,inoculum,type)

#roott1inoc6water2
roott1inoc6water2df<-as.data.frame(roott1inoc6water2.shan)
colnames(roott1inoc6water2df)[1] ="shannon"
water<-rep("water2",times=nrow(roott1inoc6water2df))
inoculum<-rep("inoc6",times=nrow(roott1inoc6water2df))
type<-rep("root",times=nrow(roott1inoc6water2df))
roott1inoc6water2dffinal<-cbind(roott1inoc6water2df,water,inoculum,type)

#roott1inoc1water3
roott1inoc1water3df<-as.data.frame(roott1inoc1water3.shan)
colnames(roott1inoc1water3df)[1] ="shannon"
water<-rep("water3",times=nrow(roott1inoc1water3df))
inoculum<-rep("inoc1",times=nrow(roott1inoc1water3df))
type<-rep("root",times=nrow(roott1inoc1water3df))
roott1inoc1water3dffinal<-cbind(roott1inoc1water3df,water,inoculum,type)

#roott1inoc4water3
roott1inoc4water3df<-as.data.frame(roott1inoc4water3.shan)
colnames(roott1inoc4water3df)[1] ="shannon"
water<-rep("water3",times=nrow(roott1inoc4water3df))
inoculum<-rep("inoc4",times=nrow(roott1inoc4water3df))
type<-rep("root",times=nrow(roott1inoc4water3df))
roott1inoc4water3dffinal<-cbind(roott1inoc4water3df,water,inoculum,type)

#roott1inoc5water3
roott1inoc5water3df<-as.data.frame(roott1inoc5water3.shan)
colnames(roott1inoc5water3df)[1] ="shannon"
water<-rep("water3",times=nrow(roott1inoc5water3df))
inoculum<-rep("inoc5",times=nrow(roott1inoc5water3df))
type<-rep("root",times=nrow(roott1inoc5water3df))
roott1inoc5water3dffinal<-cbind(roott1inoc5water3df,water,inoculum,type)

#roott1inoc6water3
roott1inoc6water3df<-as.data.frame(roott1inoc6water3.shan)
colnames(roott1inoc6water3df)[1] ="shannon"
water<-rep("water3",times=nrow(roott1inoc6water3df))
inoculum<-rep("inoc6",times=nrow(roott1inoc6water3df))
type<-rep("root",times=nrow(roott1inoc6water3df))
roott1inoc6water3dffinal<-cbind(roott1inoc6water3df,water,inoculum,type)

#shoott1inoc1water1
shoott1inoc1water1df<-as.data.frame(shoott1inoc1water1.shan)
colnames(shoott1inoc1water1df)[1] ="shannon"
water<-rep("water1",times=nrow(shoott1inoc1water1df))
inoculum<-rep("inoc1",times=nrow(shoott1inoc1water1df))
type<-rep("shoot",times=nrow(shoott1inoc1water1df))
shoott1inoc1water1dffinal<-cbind(shoott1inoc1water1df,water,inoculum,type)

#shoott1inoc4water1
shoott1inoc4water1df<-as.data.frame(shoott1inoc4water1.shan)
colnames(shoott1inoc4water1df)[1] ="shannon"
water<-rep("water1",times=nrow(shoott1inoc4water1df))
inoculum<-rep("inoc4",times=nrow(shoott1inoc4water1df))
type<-rep("shoot",times=nrow(shoott1inoc4water1df))
shoott1inoc4water1dffinal<-cbind(shoott1inoc4water1df,water,inoculum,type)

#shoott1inoc5water1
shoott1inoc5water1df<-as.data.frame(shoott1inoc5water1.shan)
colnames(shoott1inoc5water1df)[1] ="shannon"
water<-rep("water1",times=nrow(shoott1inoc5water1df))
inoculum<-rep("inoc5",times=nrow(shoott1inoc5water1df))
type<-rep("shoot",times=nrow(shoott1inoc5water1df))
shoott1inoc5water1dffinal<-cbind(shoott1inoc5water1df,water,inoculum,type)

#shoott1inoc6water1
shoott1inoc6water1df<-as.data.frame(shoott1inoc6water1.shan)
colnames(shoott1inoc6water1df)[1] ="shannon"
water<-rep("water1",times=nrow(shoott1inoc6water1df))
inoculum<-rep("inoc6",times=nrow(shoott1inoc6water1df))
type<-rep("shoot",times=nrow(shoott1inoc6water1df))
shoott1inoc6water1dffinal<-cbind(shoott1inoc6water1df,water,inoculum,type)

#shoott1inoc1water2
shoott1inoc1water2df<-as.data.frame(shoott1inoc1water2.shan)
colnames(shoott1inoc1water2df)[1] ="shannon"
water<-rep("water2",times=nrow(shoott1inoc1water2df))
inoculum<-rep("inoc1",times=nrow(shoott1inoc1water2df))
type<-rep("shoot",times=nrow(shoott1inoc1water2df))
shoott1inoc1water2dffinal<-cbind(shoott1inoc1water2df,water,inoculum,type)

#shoott1inoc4water2
shoott1inoc4water2df<-as.data.frame(shoott1inoc4water2.shan)
colnames(shoott1inoc4water2df)[1] ="shannon"
water<-rep("water2",times=nrow(shoott1inoc4water2df))
inoculum<-rep("inoc4",times=nrow(shoott1inoc4water2df))
type<-rep("shoot",times=nrow(shoott1inoc4water2df))
shoott1inoc4water2dffinal<-cbind(shoott1inoc4water2df,water,inoculum,type)

#shoott1inoc5water2
shoott1inoc5water2df<-as.data.frame(shoott1inoc5water2.shan)
colnames(shoott1inoc5water2df)[1] ="shannon"
water<-rep("water2",times=nrow(shoott1inoc5water2df))
inoculum<-rep("inoc5",times=nrow(shoott1inoc5water2df))
type<-rep("shoot",times=nrow(shoott1inoc5water2df))
shoott1inoc5water2dffinal<-cbind(shoott1inoc5water2df,water,inoculum,type)

#shoott1inoc6water2
shoott1inoc6water2df<-as.data.frame(shoott1inoc6water2.shan)
colnames(shoott1inoc6water2df)[1] ="shannon"
water<-rep("water2",times=nrow(shoott1inoc6water2df))
inoculum<-rep("inoc6",times=nrow(shoott1inoc6water2df))
type<-rep("shoot",times=nrow(shoott1inoc6water2df))
shoott1inoc6water2dffinal<-cbind(shoott1inoc6water2df,water,inoculum,type)

#shoott1inoc1water3
shoott1inoc1water3df<-as.data.frame(shoott1inoc1water3.shan)
colnames(shoott1inoc1water3df)[1] ="shannon"
water<-rep("water3",times=nrow(shoott1inoc1water3df))
inoculum<-rep("inoc1",times=nrow(shoott1inoc1water3df))
type<-rep("shoot",times=nrow(shoott1inoc1water3df))
shoott1inoc1water3dffinal<-cbind(shoott1inoc1water3df,water,inoculum,type)

#shoott1inoc4water3
shoott1inoc4water3df<-as.data.frame(shoott1inoc4water3.shan)
colnames(shoott1inoc4water3df)[1] ="shannon"
water<-rep("water3",times=nrow(shoott1inoc4water3df))
inoculum<-rep("inoc4",times=nrow(shoott1inoc4water3df))
type<-rep("shoot",times=nrow(shoott1inoc4water3df))
shoott1inoc4water3dffinal<-cbind(shoott1inoc4water3df,water,inoculum,type)

#shoott1inoc5water3
shoott1inoc5water3df<-as.data.frame(shoott1inoc5water3.shan)
colnames(shoott1inoc5water3df)[1] ="shannon"
water<-rep("water3",times=nrow(shoott1inoc5water3df))
inoculum<-rep("inoc5",times=nrow(shoott1inoc5water3df))
type<-rep("shoot",times=nrow(shoott1inoc5water3df))
shoott1inoc5water3dffinal<-cbind(shoott1inoc5water3df,water,inoculum,type)

#shoott1inoc6water3
shoott1inoc6water3df<-as.data.frame(shoott1inoc6water3.shan)
colnames(shoott1inoc6water3df)[1] ="shannon"
water<-rep("water3",times=nrow(shoott1inoc6water3df))
inoculum<-rep("inoc6",times=nrow(shoott1inoc6water3df))
type<-rep("shoot",times=nrow(shoott1inoc6water3df))
shoott1inoc6water3dffinal<-cbind(shoott1inoc6water3df,water,inoculum,type)

#shoottinoc1
shoott0inoc1df<-as.data.frame(shoott0inoc1.shan)
colnames(shoott0inoc1df)[1] ="shannon"
inoculum<-rep("inoc1",times=nrow(shoott0inoc1df))
shoott0inoc1dffinal<-cbind(shoott0inoc1df,inoculum)

#shoottinoc4
shoott0inoc4df<-as.data.frame(shoott0inoc4.shan)
colnames(shoott0inoc4df)[1] ="shannon"
inoculum<-rep("inoc4",times=nrow(shoott0inoc4df))
shoott0inoc4dffinal<-cbind(shoott0inoc4df,inoculum)

#shoottinoc5
shoott0inoc5df<-as.data.frame(shoott0inoc5.shan)
colnames(shoott0inoc5df)[1] ="shannon"
inoculum<-rep("inoc5",times=nrow(shoott0inoc5df))
shoott0inoc5dffinal<-cbind(shoott0inoc5df,inoculum)

#shoottinoc6
shoott0inoc6df<-as.data.frame(shoott0inoc6.shan)
colnames(shoott0inoc6df)[1] ="shannon"
inoculum<-rep("inoc6",times=nrow(shoott0inoc6df))
shoott0inoc6dffinal<-cbind(shoott0inoc6df,inoculum)

#now rbind them together
shanfinalbindt0<-do.call("rbind",list(shoott0inoc1dffinal,shoott0inoc4dffinal,shoott0inoc5dffinal,shoott0inoc6dffinal))

shanfinalbindt1<-do.call("rbind",list(roott1inoc1water1dffinal,roott1inoc4water1dffinal,roott1inoc5water1dffinal,roott1inoc6water1dffinal,roott1inoc1water2dffinal,roott1inoc4water2dffinal,roott1inoc5water2dffinal,roott1inoc6water2dffinal,roott1inoc1water3dffinal,roott1inoc4water3dffinal,roott1inoc5water3dffinal,roott1inoc6water3dffinal,shoott1inoc1water1dffinal,shoott1inoc4water1dffinal,shoott1inoc5water1dffinal,shoott1inoc6water1dffinal,shoott1inoc1water2dffinal,shoott1inoc4water2dffinal,shoott1inoc5water2dffinal,shoott1inoc6water2dffinal,shoott1inoc1water3dffinal,shoott1inoc4water3dffinal,shoott1inoc5water3dffinal,shoott1inoc6water3dffinal))

# add in time
shanfinalbindt0$time.point<-"T0"
shanfinalbindt0$type<-"shoot"
shanfinalbindt0$water<-"T0"

# rearrange
col_order <- c("shannon", "water", "inoculum",
               "type", "time.point")
shanfinalbindt0 <- shanfinalbindt0[, col_order]

# add time for 
shanfinalbindt1$time.point<-"T1"

# full shannon df
full.shannon.df<-rbind(shanfinalbindt0,shanfinalbindt1)

# make factors
full.shannon.df$inoculum<-as.factor(full.shannon.df$inoculum)
levels(full.shannon.df$inoculum) <- list(
  Dry="inoc1", Moderate="inoc4", Wet="inoc5", Control="inoc6")

full.shannon.df$water<-as.factor(full.shannon.df$water)
levels(full.shannon.df$water) <- list(
  T0= "T0", Low="water1", Moderate="water2", High="water3")
#### Pooled Shannon's
# T0 and T1 all sample shannon
# 'full.shannon.df' full df

# make interaction column for lattice
full.shannon.df$time.type<-interaction(full.shannon.df$time.point, full.shannon.df$type)

# set levels
full.shannon.df$time.type<-factor(full.shannon.df$time.type, 
                                  levels=c(
                                    "T0.shoot", 
                                    "T1.shoot", "T1.root"))

# make interaction column for colors
full.shannon.df$time.water<-interaction(full.shannon.df$water, full.shannon.df$time.point)

# set levels
full.shannon.df$time.water<-factor(full.shannon.df$time.water, 
                                  levels=c(
                                    "T0.T0", 
                                    "Low.T1", 
                                    "Moderate.T1", "High.T1"))


######### 3 panel
T0.shoot.shannon<-full.shannon.df[(full.shannon.df$time.type=="T0.shoot"),]
T1.shoot.shannon<-full.shannon.df[(full.shannon.df$time.type=="T1.shoot"),]
T1.root.shannon<-full.shannon.df[(full.shannon.df$time.type=="T1.root"),]

# T0 shoot
shannonviolin.T0.shoot<-ggplot(T0.shoot.shannon,
                               aes(x=water,y=shannon,fill=inoculum,color=inoculum)) + 
  geom_violin(alpha=0.2)+
  geom_point(position=position_dodge(0.9), alpha=0.7, size=0.3)+
  ylim(c(0,4.5))+
  scale_color_brewer(palette="Dark2")+
  scale_fill_brewer(palette="Dark2")+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=8),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    panel.grid = element_blank())

# T1 shoot
shannonviolin.T1.shoot<-ggplot(T1.shoot.shannon,
                               aes(x=water,y=shannon,fill=inoculum,color=inoculum)) + 
  geom_violin(position=position_dodge(0.7), alpha=0.2)+
  geom_point(position=position_dodge(0.7), alpha=0.7, size=0.3)+
  scale_color_brewer(palette="Dark2")+
  scale_fill_brewer(palette="Dark2")+
  ylim(c(0,4.5))+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=8),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    panel.grid = element_blank())

# T1 root
shannonviolin.T1.root<-ggplot(T1.root.shannon,
                               aes(x=water,y=shannon,fill=inoculum,color=inoculum)) + 
  geom_violin(position=position_dodge(0.7), alpha=0.2)+
  geom_point(position=position_dodge(0.7), alpha=0.7, size=0.3)+
  ylim(c(0,4.5))+
  scale_color_brewer(palette="Dark2")+
  scale_fill_brewer(palette="Dark2")+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=8),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=8),
    panel.grid = element_blank())


shan.legend <- get_legend(
  shannonviolin.T0.shoot + 
    theme(legend.box.margin = margin(0, 0, 0, 12)) +
    theme(legend.title=element_blank())+ # no need for a title above legend
    theme(legend.key.size = unit(0.5, "cm")))

shann_plots<-plot_grid(shannonviolin.T0.shoot + theme(legend.position = "none"),
                        shannonviolin.T1.shoot + theme(legend.position = "none"),
                        shannonviolin.T1.root + theme(legend.position = "none"),
                       shan.legend,
                       labels=c('A', 'B', 'C', ''), label_size=8, hjust=-1, vjust= 2, 
          rel_widths = c(4,8,8,2), ncol=4)

shann_plots
**Figure S6.** Shannon’s diversity of fungi (alpha diversity) before (T0) and after (T1) the start of water treatments for shoots and roots in strawberry plants subject to three different inoculum sources (dry, moderate, wet) and uninoculated controls and three water availability treatments. T0 represents 42 days post-inoculation, and T1 is 151 day post-inoculation with 47 days of water treatment exposure.

Figure S6. Shannon’s diversity of fungi (alpha diversity) before (T0) and after (T1) the start of water treatments for shoots and roots in strawberry plants subject to three different inoculum sources (dry, moderate, wet) and uninoculated controls and three water availability treatments. T0 represents 42 days post-inoculation, and T1 is 151 day post-inoculation with 47 days of water treatment exposure. 

dev.copy(pdf, "figures/FigS6.shannon.pdf", width=10, height=4.5, encod="MacRoman")
## pdf 
##   3
dev.off()
## quartz_off_screen 
##                 2

Run the stats for Shannon diversity - this is Table S7

Shannon.T0.shoot<-full.shannon.df[(full.shannon.df$time.type=="T0.shoot"),]
Shannon.T1.shoot<-full.shannon.df[(full.shannon.df$time.type=="T1.shoot"),]
Shannon.T1.root<-full.shannon.df[(full.shannon.df$time.type=="T1.root"),]

#### T0 shoots
T0.shoot.shan.mod<-lm(shannon~inoculum, Shannon.T0.shoot)
Anova(T0.shoot.shan.mod) # no effects
## Anova Table (Type II tests)
## 
## Response: shannon
##            Sum Sq Df F value Pr(>F)
## inoculum   0.4121  3  0.7536 0.5247
## Residuals 10.5723 58

#### T1 shoot
T1.shoot.shan.mod<-lm(shannon~water*inoculum, Shannon.T1.shoot)
Anova(T1.shoot.shan.mod) # water and inoc effects
## Anova Table (Type II tests)
## 
## Response: shannon
##                 Sum Sq  Df F value    Pr(>F)    
## water           2.2286   2 12.3198 1.269e-05 ***
## inoculum        1.2441   3  4.5849  0.004385 ** 
## water:inoculum  0.2894   6  0.5334  0.782095    
## Residuals      11.6677 129                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#water
posthoc.T1.shoot.b<-emmeans(T1.shoot.shan.mod,pairwise~water)
#multcomp::cld(posthoc.T1.shoot.b, Letters=letters)

#inoculum
posthoc.T1.shoot.c<-emmeans(T1.shoot.shan.mod,pairwise~inoculum)
#multcomp::cld(posthoc.T1.shoot.c, Letters=letters)

##### T1 root
T1.root.shan.mod<-lm(shannon~water*inoculum, Shannon.T1.root)
Anova(T1.root.shan.mod) #  inoc effects
## Anova Table (Type II tests)
## 
## Response: shannon
##                 Sum Sq  Df  F value Pr(>F)    
## water           0.0362   2   0.3099 0.7341    
## inoculum       22.0311   3 125.6033 <2e-16 ***
## water:inoculum  0.5949   6   1.6957 0.1269    
## Residuals       7.6592 131                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

posthoc.T1.root<-emmeans(T1.root.shan.mod,pairwise~inoculum)
multcomp::cld(posthoc.T1.root, Letters=letters)
##  inoculum emmean     SE  df lower.CL upper.CL .group
##  Control    2.45 0.0413 131     2.37     2.53  a    
##  Wet        2.95 0.0422 131     2.86     3.03   b   
##  Dry        3.26 0.0383 131     3.19     3.34    c  
##  Moderate   3.53 0.0416 131     3.45     3.61     d 
## 
## Results are averaged over the levels of: water 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 4 estimates 
## significance level used: alpha = 0.05 
## NOTE: If two or more means share the same grouping symbol,
##       then we cannot show them to be different.
##       But we also did not show them to be the same.

Venn Diagram

Start with the raw data. The venn diagram we want is split into the three water groups. Then we need original inoculum, leaves post-inoc post stress, leaves post-inoc pre stress, roots post inoc post stress. Now we need to pair down the data. Then do nrows. Then merge by the rows and then do nrows. Then we can make the euler diagrams.

T1 Shoots Venn

This is Figure S7 at T1

##########
##########
### all data
ASV.otu.tab<-as.data.frame(t(ASV.data$data_loaded))
ASV.data.all<-as.data.frame(cbind(ASV.data$map_loaded,ASV.otu.tab))

# shoots
shoot.T1.otu<-as.data.frame(t(ASV.T1.sh$data_loaded))
shoot.T1.otu.dat<-as.data.frame(cbind(ASV.T1.sh$map_loaded,shoot.T1.otu))

# roots
root.T1.otu<-as.data.frame(t(ASV.T1.rt$data_loaded))
root.T1.otu.dat<-as.data.frame(cbind(ASV.T1.rt$map_loaded,root.T1.otu))


### shoot.T1.otu.dat

## #shoots by water
shoot.T1.W1<-shoot.T1.otu.dat[(shoot.T1.otu.dat$Water=="1"),]
shoot.T1.W2<-shoot.T1.otu.dat[(shoot.T1.otu.dat$Water=="2"),]
shoot.T1.W3<-shoot.T1.otu.dat[(shoot.T1.otu.dat$Water=="3"),]


# subset to get inoculum 
# WATER 1
# Inoc 1
OTU.W1.inoc1.sh = colnames(shoot.T1.W1[shoot.T1.W1$Inoculum == "1",
              apply(shoot.T1.W1[shoot.T1.W1$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc1.sh.ASV<- OTU.W1.inoc1.sh[-1:-10] # remove factors info

# Inoc 4
OTU.W1.inoc4.sh = colnames(shoot.T1.W1[shoot.T1.W1$Inoculum == "4",
              apply(shoot.T1.W1[shoot.T1.W1$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc4.sh.ASV<- OTU.W1.inoc4.sh[-1:-10] # remove factors info

# Inoc 5
OTU.W1.inoc5.sh = colnames(shoot.T1.W1[shoot.T1.W1$Inoculum == "5",
              apply(shoot.T1.W1[shoot.T1.W1$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc5.sh.ASV<- OTU.W1.inoc5.sh[-1:-10] # remove factors info

# Inoc 6
OTU.W1.inoc6.sh = colnames(shoot.T1.W1[shoot.T1.W1$Inoculum == "6",
              apply(shoot.T1.W1[shoot.T1.W1$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc6.sh.ASV<- OTU.W1.inoc6.sh[-1:-10] # remove factors info

ASV.list.W1<-list("inoc1"=OTU.W1.inoc1.sh.ASV, "inoc4"=OTU.W1.inoc4.sh.ASV, 
          "inoc5"=OTU.W1.inoc5.sh.ASV, "inoc6"=OTU.W1.inoc6.sh.ASV)

venn(list("inoc1"=OTU.W1.inoc1.sh.ASV, "inoc4"=OTU.W1.inoc4.sh.ASV, 
          "inoc5"=OTU.W1.inoc5.sh.ASV, "inoc6"=OTU.W1.inoc6.sh.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785

######### venn diagram for Water 1
Venn.sh.T1.W1<-ggVennDiagram(ASV.list.W1, label_alpha=0) + 
  scale_fill_distiller(palette = "YlOrRd", direction = 1)+
  ggtitle("Shoots: T1, Water1")


######### 

# WATER 2
# Inoc 1
OTU.W2.inoc1.sh = colnames(shoot.T1.W2[shoot.T1.W2$Inoculum == "1",
              apply(shoot.T1.W2[shoot.T1.W2$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc1.sh.ASV<- OTU.W2.inoc1.sh[-1:-10] # remove factors info

# Inoc 4
OTU.W2.inoc4.sh = colnames(shoot.T1.W2[shoot.T1.W2$Inoculum == "4",
              apply(shoot.T1.W2[shoot.T1.W2$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc4.sh.ASV<- OTU.W2.inoc4.sh[-1:-10] # remove factors info

# Inoc 5
OTU.W2.inoc5.sh = colnames(shoot.T1.W2[shoot.T1.W2$Inoculum == "5",
              apply(shoot.T1.W2[shoot.T1.W2$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc5.sh.ASV<- OTU.W2.inoc5.sh[-1:-10] # remove factors info

# Inoc 6
OTU.W2.inoc6.sh = colnames(shoot.T1.W2[shoot.T1.W2$Inoculum == "6",
              apply(shoot.T1.W2[shoot.T1.W2$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc6.sh.ASV<- OTU.W2.inoc6.sh[-1:-10] # remove factors info

ASV.list.W2<-list("inoc1"=OTU.W2.inoc1.sh.ASV, "inoc4"=OTU.W2.inoc4.sh.ASV, 
          "inoc5"=OTU.W2.inoc5.sh.ASV, "inoc6"=OTU.W2.inoc6.sh.ASV)

venn(list("inoc1"=OTU.W2.inoc1.sh.ASV, "inoc4"=OTU.W2.inoc4.sh.ASV, 
          "inoc5"=OTU.W2.inoc5.sh.ASV, "inoc6"=OTU.W2.inoc6.sh.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785

######### venn diagram for Water 2
Venn.sh.T1.W2<-ggVennDiagram(ASV.list.W2, label_alpha=0) + 
  scale_fill_distiller(palette = "YlOrRd", direction = 1)+
  ggtitle("Shoots: T1, Water2")
######### 


# WATER 3
# Inoc 1
OTU.W3.inoc1.sh = colnames(shoot.T1.W3[shoot.T1.W3$Inoculum == "1",
              apply(shoot.T1.W3[shoot.T1.W3$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc1.sh.ASV<- OTU.W3.inoc1.sh[-1:-10] # remove factors info

# Inoc 4
OTU.W3.inoc4.sh = colnames(shoot.T1.W3[shoot.T1.W3$Inoculum == "4",
              apply(shoot.T1.W3[shoot.T1.W3$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc4.sh.ASV<- OTU.W3.inoc4.sh[-1:-10] # remove factors info

# Inoc 5
OTU.W3.inoc5.sh = colnames(shoot.T1.W3[shoot.T1.W3$Inoculum == "5",
              apply(shoot.T1.W3[shoot.T1.W3$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc5.sh.ASV<- OTU.W3.inoc5.sh[-1:-10] # remove factors info

# Inoc 6
OTU.W3.inoc6.sh = colnames(shoot.T1.W3[shoot.T1.W3$Inoculum == "6",
              apply(shoot.T1.W3[shoot.T1.W3$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc6.sh.ASV<- OTU.W3.inoc6.sh[-1:-10] # remove factors info

ASV.list.W3<-list("inoc1"=OTU.W3.inoc1.sh.ASV, "inoc4"=OTU.W3.inoc4.sh.ASV, 
          "inoc5"=OTU.W3.inoc5.sh.ASV, "inoc6"=OTU.W3.inoc6.sh.ASV)

venn(list("inoc1"=OTU.W3.inoc1.sh.ASV, "inoc4"=OTU.W3.inoc4.sh.ASV, 
          "inoc5"=OTU.W3.inoc5.sh.ASV, "inoc6"=OTU.W3.inoc6.sh.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785


######### venn diagram for Water 3
Venn.sh.T1.W3<-ggVennDiagram(ASV.list.W3, label_alpha=0) + 
  scale_fill_distiller(palette = "YlOrRd", direction = 1)+
  ggtitle("Shoots: T1, Water3")
Venn.sh.T1<-plot_grid(Venn.sh.T1.W1, Venn.sh.T1.W2, Venn.sh.T1.W3, ncol=3)
Venn.sh.T1
**Figure S7.** Venn diagrams of shoot fungi (as amplicon sequence variants – ASVs) at the end of the experiment (T1) after 151 days of exposure to three inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high).

Figure S7. Venn diagrams of shoot fungi (as amplicon sequence variants – ASVs) at the end of the experiment (T1) after 151 days of exposure to three inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high). 

dev.copy(pdf, "figures/FigS7.Venn.sh.T1.pdf", width=20, height=8, encod="MacRoman")
dev.off()
T1 Roots Venn

This is figure S8 at T1

##########
##########

# roots
root.T1.otu<-as.data.frame(t(ASV.T1.rt$data_loaded))
root.T1.otu.dat<-as.data.frame(cbind(ASV.T1.rt$map_loaded,root.T1.otu))

### root.T1.otu.dat

## #roots by water
root.T1.W1<-root.T1.otu.dat[(root.T1.otu.dat$Water=="1"),]
root.T1.W2<-root.T1.otu.dat[(root.T1.otu.dat$Water=="2"),]
root.T1.W3<-root.T1.otu.dat[(root.T1.otu.dat$Water=="3"),]

#### ROOOTS!!
# subset to get inoculum 
# WATER 1
# Inoc 1
OTU.W1.inoc1.rt = colnames(root.T1.W1[root.T1.W1$Inoculum == "1",
              apply(root.T1.W1[root.T1.W1$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc1.rt.ASV<- OTU.W1.inoc1.rt[-1:-10] # remove factors info

# Inoc 4
OTU.W1.inoc4.rt = colnames(root.T1.W1[root.T1.W1$Inoculum == "4",
              apply(root.T1.W1[root.T1.W1$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc4.rt.ASV<- OTU.W1.inoc4.rt[-1:-10] # remove factors info

# Inoc 5
OTU.W1.inoc5.rt = colnames(root.T1.W1[root.T1.W1$Inoculum == "5",
              apply(root.T1.W1[root.T1.W1$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc5.rt.ASV<- OTU.W1.inoc5.rt[-1:-10] # remove factors info

# Inoc 6
OTU.W1.inoc6.rt = colnames(root.T1.W1[root.T1.W1$Inoculum == "6",
              apply(root.T1.W1[root.T1.W1$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W1.inoc6.rt.ASV<- OTU.W1.inoc6.rt[-1:-10] # remove factors info

ASV.list.W1<-list("inoc1"=OTU.W1.inoc1.rt.ASV, "inoc4"=OTU.W1.inoc4.rt.ASV, 
          "inoc5"=OTU.W1.inoc5.rt.ASV, "inoc6"=OTU.W1.inoc6.rt.ASV)

venn(list("inoc1"=OTU.W1.inoc1.rt.ASV, "inoc4"=OTU.W1.inoc4.rt.ASV, 
          "inoc5"=OTU.W1.inoc5.rt.ASV, "inoc6"=OTU.W1.inoc6.rt.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785

######### venn diagram for Water 1
Venn.rt.T1.W1<-ggVennDiagram(ASV.list.W1, label_alpha=0) + 
  scale_fill_distiller(palette = "YlGnBu", direction = 1)+
  ggtitle("Roots: T1, Water1")


######### 

# WATER 2
# Inoc 1
OTU.W2.inoc1.rt = colnames(root.T1.W2[root.T1.W2$Inoculum == "1",
              apply(root.T1.W2[root.T1.W2$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc1.rt.ASV<- OTU.W2.inoc1.rt[-1:-10] # remove factors info

# Inoc 4
OTU.W2.inoc4.rt = colnames(root.T1.W2[root.T1.W2$Inoculum == "4",
              apply(root.T1.W2[root.T1.W2$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc4.rt.ASV<- OTU.W2.inoc4.rt[-1:-10] # remove factors info

# Inoc 5
OTU.W2.inoc5.rt = colnames(root.T1.W2[root.T1.W2$Inoculum == "5",
              apply(root.T1.W2[root.T1.W2$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc5.rt.ASV<- OTU.W2.inoc5.rt[-1:-10] # remove factors info

# Inoc 6
OTU.W2.inoc6.rt = colnames(root.T1.W2[root.T1.W2$Inoculum == "6",
              apply(root.T1.W2[root.T1.W2$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W2.inoc6.rt.ASV<- OTU.W2.inoc6.rt[-1:-10] # remove factors info

ASV.list.W2<-list("inoc1"=OTU.W2.inoc1.rt.ASV, "inoc4"=OTU.W2.inoc4.rt.ASV, 
          "inoc5"=OTU.W2.inoc5.rt.ASV, "inoc6"=OTU.W2.inoc6.rt.ASV)

venn(list("inoc1"=OTU.W2.inoc1.rt.ASV, "inoc4"=OTU.W2.inoc4.rt.ASV, 
          "inoc5"=OTU.W2.inoc5.rt.ASV, "inoc6"=OTU.W2.inoc6.rt.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785

######### venn diagram for Water 2
Venn.rt.T1.W2<-ggVennDiagram(ASV.list.W2, label_alpha=0) + 
  scale_fill_distiller(palette = "YlGnBu", direction = 1)+
  ggtitle("Roots: T1, Water2")
######### 


# WATER 3
# Inoc 1
OTU.W3.inoc1.rt = colnames(root.T1.W3[root.T1.W3$Inoculum == "1",
              apply(root.T1.W3[root.T1.W3$Inoculum == "1",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc1.rt.ASV<- OTU.W3.inoc1.rt[-1:-10] # remove factors info

# Inoc 4
OTU.W3.inoc4.rt = colnames(root.T1.W3[root.T1.W3$Inoculum == "4",
              apply(root.T1.W3[root.T1.W3$Inoculum == "4",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc4.rt.ASV<- OTU.W3.inoc4.rt[-1:-10] # remove factors info

# Inoc 5
OTU.W3.inoc5.rt = colnames(root.T1.W3[root.T1.W3$Inoculum == "5",
              apply(root.T1.W3[root.T1.W3$Inoculum == "5",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc5.rt.ASV<- OTU.W3.inoc5.rt[-1:-10] # remove factors info

# Inoc 6
OTU.W3.inoc6.rt = colnames(root.T1.W3[root.T1.W3$Inoculum == "6",
              apply(root.T1.W3[root.T1.W3$Inoculum == "6",], 
              MARGIN=2, function(x) any(x >0))])
OTU.W3.inoc6.rt.ASV<- OTU.W3.inoc6.rt[-1:-10] # remove factors info

ASV.list.W3<-list("inoc1"=OTU.W3.inoc1.rt.ASV, "inoc4"=OTU.W3.inoc4.rt.ASV, 
          "inoc5"=OTU.W3.inoc5.rt.ASV, "inoc6"=OTU.W3.inoc6.rt.ASV)

venn(list("inoc1"=OTU.W3.inoc1.rt.ASV, "inoc4"=OTU.W3.inoc4.rt.ASV, 
          "inoc5"=OTU.W3.inoc5.rt.ASV, "inoc6"=OTU.W3.inoc6.rt.ASV))
## 4 set Venn diagram 
## 
##          h k   a b    phi
## inoc1 -0.8 0 1.2 2  0.785
## inoc4  0.8 0 1.2 2 -0.785
## inoc5  0.0 1 1.2 2  0.785
## inoc6  0.0 1 1.2 2 -0.785

######### venn diagram for Water 3
Venn.rt.T1.W3<-ggVennDiagram(ASV.list.W3, label_alpha=0) + 
  scale_fill_distiller(palette = "YlGnBu", direction = 1)+
  ggtitle("Roots: T1, Water3")
Venn.rt.T1<-plot_grid(Venn.rt.T1.W1, Venn.rt.T1.W2, Venn.rt.T1.W3, ncol=3)
Venn.rt.T1
**Figure S8.** Venn diagrams of root fungi (as amplicon sequence variants – ASVs) at the end of the experiment (T1) after 151 days of exposure to three inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high)

Figure S8. Venn diagrams of root fungi (as amplicon sequence variants – ASVs) at the end of the experiment (T1) after 151 days of exposure to three inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high) 

dev.copy(pdf, "figures/FigS8.Venn.rt.T1.pdf", width=20, height=8, encod="MacRoman")
dev.off()

Relative abundance

Make stacked bar plots of the relative abundance of fungal taxa across treatments:

# let's refilter again to just root t1, shoot t1, and shoot t0
# Pair down the other data. We will have to hellinger transform this data too
# 'ASV.data' is the final df

## data subset already above
#ASV.T0.sh  # 62 samples, T0 shoots

#ASV.T1  # 284 samples, T1 all data
#ASV.T1.rt  # 143 samples, T1 root
#ASV.T1.sh # 141 samples, T1 shoots


#### Time 1
# all T1
inputstep5 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T1')
# roots
roott1 = filter_data(inputstep5, 'Type', keep_vals = 'Root')
#shoots
shoott1 = filter_data(inputstep5, 'Type', keep_vals = 'Shoot')

#### Time 0
time0 = filter_data(ASV.data,'TimePoint',keep_vals = 'T0')
shoott0 = filter_data(time0, 'Type', keep_vals = 'Shoot')


#transformation
library(labdsv)
otuTAB<-shoott1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
shoott1$data_loaded<-t(hell)

otuTAB<-roott1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
roott1$data_loaded<-t(hell)

otuTAB<-shoott0$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
shoott0$data_loaded<-t(hell)

Shoots T1: Family relative abund.

Now we can make t1 shoots by inoculation and water treatment for shoots and roots at the Family level.

#let's refilter again just in case but include the control too
#Pair down the other data. We will have to hellinger transform this data too
library(mctoolsr)
library(ggplot2)
library(labdsv)
inputstep5 = filter_data(input, 'TimePoint', keep_vals = 'T1')
shoots = filter_data(inputstep5, 'Type', keep_vals = 'Shoot')

water1 = filter_data(shoots, 'Water', keep_vals = '1')
water1inoc1 = filter_data(water1, 'Inoculum', keep_vals = '1')
water1inoc4 = filter_data(water1, 'Inoculum', keep_vals = '4')
water1inoc5 = filter_data(water1, 'Inoculum', keep_vals = '5')
water1inoc6 = filter_data(water1, 'Inoculum', keep_vals = '6')

water2 = filter_data(shoots, 'Water', keep_vals = '2')
water2inoc1 = filter_data(water2, 'Inoculum', keep_vals = '1')
water2inoc4 = filter_data(water2, 'Inoculum', keep_vals = '4')
water2inoc5 = filter_data(water2, 'Inoculum', keep_vals = '5')
water2inoc6 = filter_data(water2, 'Inoculum', keep_vals = '6')

water3 = filter_data(shoots, 'Water', keep_vals = '3')
water3inoc1 = filter_data(water3, 'Inoculum', keep_vals = '1')
water3inoc4 = filter_data(water3, 'Inoculum', keep_vals = '4')
water3inoc5 = filter_data(water3, 'Inoculum', keep_vals = '5')
water3inoc6 = filter_data(water3, 'Inoculum', keep_vals = '6')

#transformation
otuTAB<-water1inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc1$data_loaded<-t(hell)

otuTAB<-water1inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc4$data_loaded<-t(hell)

otuTAB<-water1inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc5$data_loaded<-t(hell)

otuTAB<-water1inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc6$data_loaded<-t(hell)

otuTAB<-water2inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc1$data_loaded<-t(hell)

otuTAB<-water2inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc4$data_loaded<-t(hell)

otuTAB<-water2inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc5$data_loaded<-t(hell)

otuTAB<-water2inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc6$data_loaded<-t(hell)

otuTAB<-water3inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc1$data_loaded<-t(hell)

otuTAB<-water3inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc4$data_loaded<-t(hell)

otuTAB<-water3inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc5$data_loaded<-t(hell)

otuTAB<-water3inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc6$data_loaded<-t(hell)

#############
# We will need to make a large dataframe here. View after melting to see how many columns to make.
# This will be family for SHOOOOOTS

library(reshape2)
water1inoc1_tax_sum_family = summarize_taxonomy(water1inoc1, level = 5, report_higher_tax = FALSE)
water1inoc1_tax_sum_family$family <-row.names(water1inoc1_tax_sum_family)
water1inoc1_melt<- melt(water1inoc1_tax_sum_family, id.var="family")
nrow(water1inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water1inoc1_melt))
water<-rep("water1",times=nrow(water1inoc1_melt))
water1inoc1_melt<-cbind(water1inoc1_melt,inoculum,water)

water1inoc4_tax_sum_family = summarize_taxonomy(water1inoc4, level = 5, report_higher_tax = FALSE)
water1inoc4_tax_sum_family$family <-row.names(water1inoc4_tax_sum_family)
water1inoc4_melt<- melt(water1inoc4_tax_sum_family, id.var="family")
nrow(water1inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water1inoc4_melt))
water<-rep("water1",times=nrow(water1inoc4_melt))
water1inoc4_melt<-cbind(water1inoc4_melt,inoculum,water)

water1inoc5_tax_sum_family = summarize_taxonomy(water1inoc5, level = 5, report_higher_tax = FALSE)
water1inoc5_tax_sum_family$family <-row.names(water1inoc5_tax_sum_family)
water1inoc5_melt<- melt(water1inoc5_tax_sum_family, id.var="family")
nrow(water1inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water1inoc5_melt))
water<-rep("water1",times=nrow(water1inoc5_melt))
water1inoc5_melt<-cbind(water1inoc5_melt,inoculum,water)

water1inoc6_tax_sum_family = summarize_taxonomy(water1inoc6, level = 5, report_higher_tax = FALSE)
water1inoc6_tax_sum_family$family <-row.names(water1inoc6_tax_sum_family)
water1inoc6_melt<- melt(water1inoc6_tax_sum_family, id.var="family")
nrow(water1inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water1inoc6_melt))
water<-rep("water1",times=nrow(water1inoc6_melt))
water1inoc6_melt<-cbind(water1inoc6_melt,inoculum,water)

water2inoc1_tax_sum_family = summarize_taxonomy(water2inoc1, level = 5, report_higher_tax = FALSE)
water2inoc1_tax_sum_family$family <-row.names(water2inoc1_tax_sum_family)
water2inoc1_melt<- melt(water2inoc1_tax_sum_family, id.var="family")
nrow(water2inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water2inoc1_melt))
water<-rep("water2",times=nrow(water2inoc1_melt))
water2inoc1_melt<-cbind(water2inoc1_melt,inoculum,water)

water2inoc4_tax_sum_family = summarize_taxonomy(water2inoc4, level = 5, report_higher_tax = FALSE)
water2inoc4_tax_sum_family$family <-row.names(water2inoc4_tax_sum_family)
water2inoc4_melt<- melt(water2inoc4_tax_sum_family, id.var="family")
nrow(water2inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water2inoc4_melt))
water<-rep("water2",times=nrow(water2inoc4_melt))
water2inoc4_melt<-cbind(water2inoc4_melt,inoculum,water)

water2inoc5_tax_sum_family = summarize_taxonomy(water2inoc5, level = 5, report_higher_tax = FALSE)
water2inoc5_tax_sum_family$family <-row.names(water2inoc5_tax_sum_family)
water2inoc5_melt<- melt(water2inoc5_tax_sum_family, id.var="family")
nrow(water2inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water2inoc5_melt))
water<-rep("water2",times=nrow(water2inoc5_melt))
water2inoc5_melt<-cbind(water2inoc5_melt,inoculum,water)

water2inoc6_tax_sum_family = summarize_taxonomy(water2inoc6, level = 5, report_higher_tax = FALSE)
water2inoc6_tax_sum_family$family <-row.names(water2inoc6_tax_sum_family)
water2inoc6_melt<- melt(water2inoc6_tax_sum_family, id.var="family")
nrow(water2inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water2inoc6_melt))
water<-rep("water2",times=nrow(water2inoc6_melt))
water2inoc6_melt<-cbind(water2inoc6_melt,inoculum,water)

water3inoc1_tax_sum_family = summarize_taxonomy(water3inoc1, level = 5, report_higher_tax = FALSE)
water3inoc1_tax_sum_family$family <-row.names(water3inoc1_tax_sum_family)
water3inoc1_melt<- melt(water3inoc1_tax_sum_family, id.var="family")
nrow(water3inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water3inoc1_melt))
water<-rep("water3",times=nrow(water3inoc1_melt))
water3inoc1_melt<-cbind(water3inoc1_melt,inoculum,water)

water3inoc4_tax_sum_family = summarize_taxonomy(water3inoc4, level = 5, report_higher_tax = FALSE)
water3inoc4_tax_sum_family$family <-row.names(water3inoc4_tax_sum_family)
water3inoc4_melt<- melt(water3inoc4_tax_sum_family, id.var="family")
nrow(water3inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water3inoc4_melt))
water<-rep("water3",times=nrow(water3inoc4_melt))
water3inoc4_melt<-cbind(water3inoc4_melt,inoculum,water)

water3inoc5_tax_sum_family = summarize_taxonomy(water3inoc5, level = 5, report_higher_tax = FALSE)
water3inoc5_tax_sum_family$family <-row.names(water3inoc5_tax_sum_family)
water3inoc5_melt<- melt(water3inoc5_tax_sum_family, id.var="family")
nrow(water3inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water3inoc5_melt))
water<-rep("water3",times=nrow(water3inoc5_melt))
water3inoc5_melt<-cbind(water3inoc5_melt,inoculum,water)

water3inoc6_tax_sum_family = summarize_taxonomy(water3inoc6, level = 5, report_higher_tax = FALSE)
water3inoc6_tax_sum_family$family <-row.names(water3inoc6_tax_sum_family)
water3inoc6_melt<- melt(water3inoc6_tax_sum_family, id.var="family")
nrow(water3inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water3inoc6_melt))
water<-rep("water3",times=nrow(water3inoc6_melt))
water3inoc6_melt<-cbind(water3inoc6_melt,inoculum,water)

familyshootfinalbind<-do.call("rbind",list(water1inoc1_melt,water1inoc4_melt,water1inoc5_melt,water1inoc6_melt,water2inoc1_melt,water2inoc4_melt,water2inoc5_melt,water2inoc6_melt,water3inoc1_melt,water3inoc4_melt,water3inoc5_melt,water3inoc6_melt))

familyshootfinalbind$family <- gsub('f__','',familyshootfinalbind$family) # remove the family 'f__'
#plot it: shoots T1 family relative abundance

library(ggthemes)
familyshootfinalbind.agg<-aggregate(value~inoculum*water*family, data=familyshootfinalbind, "sum")

shootsfamily<-ggplot(familyshootfinalbind.agg, aes(x = inoculum, y = value, fill = family)) + 
  geom_bar(stat="identity",position = "fill") + 
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = colorRampPalette(solarized_pal()(8))(90)) + 
  ggtitle("Shoots Family Graphs")+
  facet_grid(.~water,scales ="free",space="free")+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title.y= element_blank(),
    axis.title.x= element_blank(),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=18),
    panel.grid = element_blank(),
    legend.key.size = unit(1, 'mm'),
    legend.text = element_text(color='black',size=4))

shootsfamily
ggsave("figures/FigS9.Shootssfamily.pdf", height=7, width=14)

leg.st<-get_legend(
  shootsfamily + 
    theme(legend.box.margin = margin(0, 0, 0, 20)) +
    theme(legend.title=element_blank())+ # no need for a title above legend
    theme(legend.key.size = unit(0.5, "cm")))
leg.st
ggsave("figures/FigS9x.Shootssfamily_leg.pdf", height=5, width=6)
plot_grid(shootsfamily + guides(fill="none"),
leg.st, nrow=2)
**Figure S9.** Relative abundance of fungal families present in shoots of strawberry plants at the end of the experiment (T1) after 151 days of exposure to three soil inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high).

Figure S9. Relative abundance of fungal families present in shoots of strawberry plants at the end of the experiment (T1) after 151 days of exposure to three soil inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high).

Roots T1: Family relative abund.

Now we can make t1 roots by inoculation and water treatment

#let's refilter again just in case but include the control too
#Pair down the other data. We will have to hellinger transform this data too

inputstep5 = filter_data(ASV.data, 'TimePoint', keep_vals = 'T1')

roots = filter_data(inputstep5, 'Type', keep_vals = 'Root')
water1 = filter_data(roots, 'Water', keep_vals = '1')
water1inoc1 = filter_data(water1, 'Inoculum', keep_vals = '1')
water1inoc4 = filter_data(water1, 'Inoculum', keep_vals = '4')
water1inoc5 = filter_data(water1, 'Inoculum', keep_vals = '5')
water1inoc6 = filter_data(water1, 'Inoculum', keep_vals = '6')

water2 = filter_data(roots, 'Water', keep_vals = '2')
water2inoc1 = filter_data(water2, 'Inoculum', keep_vals = '1')
water2inoc4 = filter_data(water2, 'Inoculum', keep_vals = '4')
water2inoc5 = filter_data(water2, 'Inoculum', keep_vals = '5')
water2inoc6 = filter_data(water2, 'Inoculum', keep_vals = '6')

water3 = filter_data(roots, 'Water', keep_vals = '3')
water3inoc1 = filter_data(water3, 'Inoculum', keep_vals = '1')
water3inoc4 = filter_data(water3, 'Inoculum', keep_vals = '4')
water3inoc5 = filter_data(water3, 'Inoculum', keep_vals = '5')
water3inoc6 = filter_data(water3, 'Inoculum', keep_vals = '6')

#transformation
otuTAB<-water1inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc1$data_loaded<-t(hell)

otuTAB<-water1inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc4$data_loaded<-t(hell)

otuTAB<-water1inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc5$data_loaded<-t(hell)

otuTAB<-water1inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water1inoc6$data_loaded<-t(hell)

otuTAB<-water2inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc1$data_loaded<-t(hell)

otuTAB<-water2inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc4$data_loaded<-t(hell)

otuTAB<-water2inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc5$data_loaded<-t(hell)

otuTAB<-water2inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water2inoc6$data_loaded<-t(hell)

otuTAB<-water3inoc1$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc1$data_loaded<-t(hell)

otuTAB<-water3inoc4$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc4$data_loaded<-t(hell)

otuTAB<-water3inoc5$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc5$data_loaded<-t(hell)

otuTAB<-water3inoc6$data_loaded
otuTAB <- as.data.frame (otuTAB)
otuTABtranspose<-t(otuTAB)
hell<-hellinger(otuTABtranspose)
water3inoc6$data_loaded<-t(hell)

# We will need to make a large dataframe here. View after melting to see how many columns to make. 
# This will be family for ROOTS

library(reshape2)
water1inoc1_tax_sum_family = summarize_taxonomy(water1inoc1, level = 5, report_higher_tax = FALSE)
water1inoc1_tax_sum_family$family <-row.names(water1inoc1_tax_sum_family)
water1inoc1_melt<- melt(water1inoc1_tax_sum_family, id.var="family")
nrow(water1inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water1inoc1_melt))
water<-rep("water1",times=nrow(water1inoc1_melt))
water1inoc1_melt<-cbind(water1inoc1_melt,inoculum,water)

water1inoc4_tax_sum_family = summarize_taxonomy(water1inoc4, level = 5, report_higher_tax = FALSE)
water1inoc4_tax_sum_family$family <-row.names(water1inoc4_tax_sum_family)
water1inoc4_melt<- melt(water1inoc4_tax_sum_family, id.var="family")
nrow(water1inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water1inoc4_melt))
water<-rep("water1",times=nrow(water1inoc4_melt))
water1inoc4_melt<-cbind(water1inoc4_melt,inoculum,water)

water1inoc5_tax_sum_family = summarize_taxonomy(water1inoc5, level = 5, report_higher_tax = FALSE)
water1inoc5_tax_sum_family$family <-row.names(water1inoc5_tax_sum_family)
water1inoc5_melt<- melt(water1inoc5_tax_sum_family, id.var="family")
nrow(water1inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water1inoc5_melt))
water<-rep("water1",times=nrow(water1inoc5_melt))
water1inoc5_melt<-cbind(water1inoc5_melt,inoculum,water)

water1inoc6_tax_sum_family = summarize_taxonomy(water1inoc6, level = 5, report_higher_tax = FALSE)
water1inoc6_tax_sum_family$family <-row.names(water1inoc6_tax_sum_family)
water1inoc6_melt<- melt(water1inoc6_tax_sum_family, id.var="family")
nrow(water1inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water1inoc6_melt))
water<-rep("water1",times=nrow(water1inoc6_melt))
water1inoc6_melt<-cbind(water1inoc6_melt,inoculum,water)

water2inoc1_tax_sum_family = summarize_taxonomy(water2inoc1, level = 5, report_higher_tax = FALSE)
water2inoc1_tax_sum_family$family <-row.names(water2inoc1_tax_sum_family)
water2inoc1_melt<- melt(water2inoc1_tax_sum_family, id.var="family")
nrow(water2inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water2inoc1_melt))
water<-rep("water2",times=nrow(water2inoc1_melt))
water2inoc1_melt<-cbind(water2inoc1_melt,inoculum,water)

water2inoc4_tax_sum_family = summarize_taxonomy(water2inoc4, level = 5, report_higher_tax = FALSE)
water2inoc4_tax_sum_family$family <-row.names(water2inoc4_tax_sum_family)
water2inoc4_melt<- melt(water2inoc4_tax_sum_family, id.var="family")
nrow(water2inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water2inoc4_melt))
water<-rep("water2",times=nrow(water2inoc4_melt))
water2inoc4_melt<-cbind(water2inoc4_melt,inoculum,water)

water2inoc5_tax_sum_family = summarize_taxonomy(water2inoc5, level = 5, report_higher_tax = FALSE)
water2inoc5_tax_sum_family$family <-row.names(water2inoc5_tax_sum_family)
water2inoc5_melt<- melt(water2inoc5_tax_sum_family, id.var="family")
nrow(water2inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water2inoc5_melt))
water<-rep("water2",times=nrow(water2inoc5_melt))
water2inoc5_melt<-cbind(water2inoc5_melt,inoculum,water)

water2inoc6_tax_sum_family = summarize_taxonomy(water2inoc6, level = 5, report_higher_tax = FALSE)
water2inoc6_tax_sum_family$family <-row.names(water2inoc6_tax_sum_family)
water2inoc6_melt<- melt(water2inoc6_tax_sum_family, id.var="family")
nrow(water2inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water2inoc6_melt))
water<-rep("water2",times=nrow(water2inoc6_melt))
water2inoc6_melt<-cbind(water2inoc6_melt,inoculum,water)

water3inoc1_tax_sum_family = summarize_taxonomy(water3inoc1, level = 5, report_higher_tax = FALSE)
water3inoc1_tax_sum_family$family <-row.names(water3inoc1_tax_sum_family)
water3inoc1_melt<- melt(water3inoc1_tax_sum_family, id.var="family")
nrow(water3inoc1_melt)
inoculum<-rep("inoc1",times=nrow(water3inoc1_melt))
water<-rep("water3",times=nrow(water3inoc1_melt))
water3inoc1_melt<-cbind(water3inoc1_melt,inoculum,water)

water3inoc4_tax_sum_family = summarize_taxonomy(water3inoc4, level = 5, report_higher_tax = FALSE)
water3inoc4_tax_sum_family$family <-row.names(water3inoc4_tax_sum_family)
water3inoc4_melt<- melt(water3inoc4_tax_sum_family, id.var="family")
nrow(water3inoc4_melt)
inoculum<-rep("inoc4",times=nrow(water3inoc4_melt))
water<-rep("water3",times=nrow(water3inoc4_melt))
water3inoc4_melt<-cbind(water3inoc4_melt,inoculum,water)

water3inoc5_tax_sum_family = summarize_taxonomy(water3inoc5, level = 5, report_higher_tax = FALSE)
water3inoc5_tax_sum_family$family <-row.names(water3inoc5_tax_sum_family)
water3inoc5_melt<- melt(water3inoc5_tax_sum_family, id.var="family")
nrow(water3inoc5_melt)
inoculum<-rep("inoc5",times=nrow(water3inoc5_melt))
water<-rep("water3",times=nrow(water3inoc5_melt))
water3inoc5_melt<-cbind(water3inoc5_melt,inoculum,water)

water3inoc6_tax_sum_family = summarize_taxonomy(water3inoc6, level = 5, report_higher_tax = FALSE)
water3inoc6_tax_sum_family$family <-row.names(water3inoc6_tax_sum_family)
water3inoc6_melt<- melt(water3inoc6_tax_sum_family, id.var="family")
nrow(water3inoc6_melt)
inoculum<-rep("inoc6",times=nrow(water3inoc6_melt))
water<-rep("water3",times=nrow(water3inoc6_melt))
water3inoc6_melt<-cbind(water3inoc6_melt,inoculum,water)

familyrootfinalbind<-do.call("rbind",list(water1inoc1_melt,water1inoc4_melt,water1inoc5_melt,water1inoc6_melt,water2inoc1_melt,water2inoc4_melt,water2inoc5_melt,water2inoc6_melt,water3inoc1_melt,water3inoc4_melt,water3inoc5_melt,water3inoc6_melt))

familyrootfinalbind$family <- gsub('f__','',familyrootfinalbind$family) # remove the family 'f__'
library(ggthemes)

# aggregate to fix formatting and avoid weird white lines

familyrootfinalbind.agg<-aggregate(value~inoculum*water*family, data=familyrootfinalbind, "sum")

rootsfamily<-ggplot(familyrootfinalbind.agg, aes(x = inoculum, y = value, fill = family)) + 
  geom_bar(stat="identity",position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = colorRampPalette(solarized_pal()(8))(90)) + 
  ggtitle("Roots Family Graphs")+
  facet_grid(.~water,scales ="free",space="free")+
  theme_bw() + theme(
    axis.text = element_text(color='black',size=10),
    axis.title.y= element_blank(),
    axis.title.x= element_blank(),
    axis.title = element_text(color='black',size=12),
    axis.ticks = element_line(color='black'),
    legend.title = element_text(size=18),
    panel.grid = element_blank(),
    legend.key.size = unit(1, 'mm'),
    legend.text = element_text(color='black',size=4))

rootsfamily
ggsave("figures/FigS10.Rootsfamily.pdf", height=7, width=14)

leg.rt<-get_legend(
  rootsfamily + 
    theme(legend.box.margin = margin(0, 0, 0, 20)) +
    theme(legend.title=element_blank())+ # no need for a title above legend
    theme(legend.key.size = unit(0.5, "cm")))
leg.rt
ggsave("figures/FigS10x.Rootsfamily_leg.pdf", height=5, width=6)
plot_grid(rootsfamily + guides(fill="none"),
leg.rt, nrow=2)
**Figure S10.** Relative abundance of fungal families present in roots of strawberry plants at the end of the experiment (T1) after 151 days of exposure to three soil inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high).

Figure S10. Relative abundance of fungal families present in roots of strawberry plants at the end of the experiment (T1) after 151 days of exposure to three soil inoculum sources (dry, moderate, wet) and uninoculated controls, including 47 days of exposure to three water availability treatments (low, moderate, high).